OLD SHAPE¶

Apenas eco-regiões

%%HTML
<h1> Índice</h1>
<h3>
<a href="#1">clicar aqui!</a> para ir para as primeiras figuras
</h3><br>
<h3> outras figuras:</h3><br>

<a href="#varintermonbio">Variação inter-anual por mês em cada bioma</a>
<br>
<a href="#varintrabio"> Variação intra_anual em cada bioma</a>

Lost Pixels

Mapas:

Total por ecorregiões

Total por biomas

Variação inter-anual por ecoregiões

Variação inter-anual por biomas

# imports
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon
from matplotlib.collections import PatchCollection
import numpy as np
import matplotlib.cm as cm
import matplotlib.colors as cls
from pandas import Series, DataFrame
from mpl_toolkits.axes_grid1.inset_locator import inset_axes, zoomed_inset_axes, mark_inset
import matplotlib.ticker as ticker
from collections import defaultdict, namedtuple
from pandas import ExcelWriter
import xlsxwriter
import pandas as pd

import calendar

plt.rcParams["font.family"] = "Times New Roman"

def get_ecos():
    m = Basemap(projection='cyl')
    shpf = m.readshapefile('ecoregions2017/ecoregions2017','ecos',linewidth=0.1)
    return m.ecos_info, m.ecos

months = "Jan Fev Mar Abr Mai Jun Jul Ago Set Out Nov Dez".split(' ')

ecos_info, ecos = get_ecos()


Eco = namedtuple('Eco', ['name', 'biome_name', 'biome_num', 'area'])
info_dict = {eco['ECO_ID']:Eco(eco['ECO_NAME'],eco['BIOME_NAME'],eco['BIOME_NUM'], eco['SHAPE_AREA']) for eco in ecos_info}

eco_names = []
for i in range(847):
    name = info_dict[i].name
    if type(name) == bytes:
        name = name.decode('latin-1') 
    eco_names.append(name)

biome_names = []
for i in range(847):
    biome_names.append(info_dict[i].biome_name)

biome_nums = []
for i in range(847):
    biome_nums.append(info_dict[i].biome_num)
    
areas = []
for i in range(847):
    areas.append(info_dict[i].area)

biome_nums[0] = 0.0

result = {}
for i in range(1,18):
    suf = '20{0:02d}'.format(i)
    in_file= 'result' + suf
    result[suf] = DataFrame(np.load(in_file), columns =months)

for year in result:
    result[year].index.name ='Eco_Id'

for year in result:
    result[year] =  result[year].transpose()

head = DataFrame()
head = head.assign(ECO_NAME = eco_names, BIOME_NUM=biome_nums,BIOME_NAME=biome_names,AREAS= areas).transpose()

result['Atributos'] = head

df_finall = pd.concat(result)

df_finall

Não dividido por área¶

Total por Bioma¶

df_finall.loc['Atributos'].loc['BIOME_NUM']

Eco_Id
0       0
1       1
2       1
3       1
4       1
5       1
6       1
7       1
8       1
9       1
10      1
11      1
12      1
13      1
14      1
15      1
16      1
17      1
18      1
19      1
20      1
21      1
22      1
23      1
24      1
25      1
26      1
27      1
28      1
29      1
       ..
817    13
818    13
819    13
820    13
821    13
822    13
823    13
824    13
825    13
826    13
827    13
828    13
829    13
830    13
831    13
832    13
833    13
834    13
835    13
836    13
837    13
838    13
839    13
840    13
841    13
842    13
843    13
844    13
845    13
846    13
Name: BIOME_NUM, Length: 847, dtype: object

df_finall[:][:12*17]

biome_grouped = df_finall[:][:12*17].groupby(df_finall.loc['Atributos'].loc['BIOME_NUM'], axis=1)

biome_grouped.sum()

#writer = ExcelWriter('results_newstyle2.xlsx',engine='xlsxwriter')
#df_finall.to_excel(writer,sheet_name='Burned Pixels',engine='xlsxwriter')
#writer.save()

total_biome = biome_grouped.sum().sum()

RESULTADO¶

total_biome

BIOME_NUM
0.0          4837.0
1.0      21872218.0
2.0       6702933.0
3.0        619787.0
4.0       6439650.0
5.0       1063191.0
6.0       4981719.0
7.0     237984112.0
8.0      11630411.0
9.0      13816076.0
10.0      1670577.0
11.0       437043.0
12.0      1623004.0
13.0     22379896.0
14.0       302628.0
dtype: float64

Total por Ecoregião¶

total_eco = df_finall.iloc[:12*17].sum()

total_eco

Eco_Id
0         4837.0
1       702891.0
2       225592.0
3       149934.0
4         1038.0
5        37142.0
6        22712.0
7        52385.0
8        60509.0
9        31478.0
10       18235.0
11      325106.0
12        7609.0
13           0.0
14      136999.0
15        1297.0
16       25166.0
17      138000.0
18     1967024.0
19      255883.0
20         548.0
21           0.0
22        8764.0
23      126461.0
24     1103731.0
25      561498.0
26      388095.0
27           3.0
28     1698135.0
29      123833.0
         ...    
817     363213.0
818      91760.0
819      37002.0
820       6250.0
821          0.0
822       1424.0
823       6901.0
824        494.0
825        159.0
826      13431.0
827      39677.0
828    2355221.0
829      11675.0
830      13690.0
831      36318.0
832          0.0
833        511.0
834       4485.0
835        215.0
836         10.0
837        314.0
838       4247.0
839          0.0
840         29.0
841      42379.0
842        324.0
843       3099.0
844          0.0
845         25.0
846          0.0
Length: 847, dtype: float64

Dividido por área¶

Total por ecoregião¶

total_eco_area = total_eco/(areas)

Total por Bioma¶

df_finall[:][12*17:-1:2]

df_finall[:][12*17 +2: 12*17 +3]

df_finall.loc['Atributos'].loc['AREAS']

Eco_Id
0        6487.85
1        12.2498
2        3.09709
3        33.5636
4        0.17066
5        15.3531
6        1.68336
7        4.22837
8        5.01093
9       0.890325
10       7.50154
11       15.4412
12       5.54681
13     0.0251181
14       2.55149
15      0.201715
16        1.0287
17       9.59984
18       17.1053
19       2.72629
20      0.427807
21     0.0929414
22        1.1698
23       5.48488
24       41.6676
25       11.6915
26       35.1508
27     0.0838619
28       12.6843
29       10.4078
         ...    
817      75.4713
818       9.8943
819      60.3621
820      56.3572
821      1.53083
822      137.457
823      2.35131
824      31.6342
825      16.0622
826      19.2962
827      35.2106
828      82.3318
829      2.70801
830      19.9401
831      43.9754
832     0.735171
833      148.952
834      9.25653
835      19.6334
836       5.2527
837      27.7603
838      25.9865
839       3.4904
840      31.7648
841      32.1171
842      253.974
843      78.3785
844      7.13004
845      68.5239
846      22.7455
Name: AREAS, Length: 847, dtype: object

df_finall.loc['Atributos'].loc['AREAS'].groupby(biome_nums).sum()

0.0     6487.848621
1.0     1632.472566
2.0      327.121754
3.0       60.219125
4.0     1444.893009
5.0      444.991141
6.0     2522.867989
7.0     1796.152160
8.0     1198.433201
9.0      105.597352
10.0     471.456944
11.0    1993.369285
12.0     329.432038
13.0    2488.539746
14.0      27.665056
Name: AREAS, dtype: float64

df_finall.T[df_finall.T['Atributos']['BIOME_NUM'] == 14]['Atributos']['AREAS'].sum()

27.665055532789804

areas_biome = df_finall.loc['Atributos'].loc['AREAS'].groupby(biome_nums).sum()

areas_biome

0.0     6487.848621
1.0     1632.472566
2.0      327.121754
3.0       60.219125
4.0     1444.893009
5.0      444.991141
6.0     2522.867989
7.0     1796.152160
8.0     1198.433201
9.0      105.597352
10.0     471.456944
11.0    1993.369285
12.0     329.432038
13.0    2488.539746
14.0      27.665056
Name: AREAS, dtype: float64

RESULTADO¶

total_biome_area = total_biome/areas_biome

total_biome.iloc[14]/areas_biome[14]

10938.998464735138

302628.0/27.665056

10938.998279996253

SOMAS MENSAIS¶

Por ecoregião¶

df_finall[1: 12*17:12]

Eco_Id
0         114.0
1       39753.0
2       28266.0
3        7100.0
4           0.0
5         382.0
6        4368.0
7       15774.0
8       17994.0
9          82.0
10       1213.0
11      94081.0
12       3736.0
13          0.0
14      15642.0
15         78.0
16         21.0
17         59.0
18         75.0
19       3945.0
20          0.0
21          0.0
22       1800.0
23      28586.0
24     202063.0
25      32492.0
26      50314.0
27          1.0
28        276.0
29      15963.0
         ...   
817      2204.0
818      7023.0
819     11749.0
820       582.0
821         0.0
822         0.0
823      1074.0
824         0.0
825         0.0
826         0.0
827       232.0
828       353.0
829      1204.0
830      1484.0
831       861.0
832         0.0
833        64.0
834        20.0
835       202.0
836         0.0
837         0.0
838       777.0
839         0.0
840         0.0
841      6551.0
842         0.0
843      1899.0
844         0.0
845         2.0
846         0.0
Length: 847, dtype: float64

months

['Jan',
 'Fev',
 'Mar',
 'Abr',
 'Mai',
 'Jun',
 'Jul',
 'Ago',
 'Set',
 'Out',
 'Nov',
 'Dez']

monthly = [ df_finall[n: 12*17:12].sum()/areas for n in range(12)]

RESULTADO¶

monthly

[Eco_Id
 0          0.023428
 1       2317.345349
 2       8149.267504
 3         59.320297
 4          0.000000
 5         15.762279
 6       7294.335597
 7       6441.491295
 8       3084.058386
 9        139.274984
 10        33.726413
 11      7769.135310
 12        66.164109
 13         0.000000
 14     11038.273505
 15       287.534440
 16        51.521157
 17       293.546527
 18        52.556688
 19      1268.754934
 20         0.000000
 21         0.000000
 22      2939.816309
 23     11913.288339
 24      6395.825687
 25      1857.676390
 26      1981.720437
 27         0.000000
 28        64.252635
 29       479.542527
            ...     
 817        5.180774
 818       78.327908
 819       39.677186
 820       17.832687
 821        0.000000
 822        0.000000
 823      409.132801
 824        0.000000
 825        0.000000
 826        0.000000
 827        0.000000
 828        0.000000
 829       34.711889
 830       34.453112
 831       10.005595
 832        0.000000
 833        0.120844
 834        4.321276
 835        0.000000
 836        0.000000
 837        0.000000
 838       10.967231
 839        0.000000
 840        0.000000
 841      173.895088
 842        0.003937
 843        3.215166
 844        0.000000
 845        0.000000
 846        0.000000
 Length: 847, dtype: float64, Eco_Id
 0         0.017571
 1      3245.197789
 2      9126.637160
 3       211.538979
 4         0.000000
 5        24.880953
 6      2594.808852
 7      3730.516712
 8      3590.950343
 9        92.101199
 10      161.700156
 11     6092.843905
 12      673.539815
 13        0.000000
 14     6130.545170
 15      386.684246
 16       20.414043
 17        6.145935
 18        4.384596
 19     1447.018854
 20        0.000000
 21        0.000000
 22     1538.723279
 23     5211.778774
 24     4849.398031
 25     2779.116039
 26     1431.376880
 27       11.924364
 28       21.759175
 29     1533.748219
           ...     
 817      29.203136
 818     709.802452
 819     194.641864
 820      10.326989
 821       0.000000
 822       0.000000
 823     456.765726
 824       0.000000
 825       0.000000
 826       0.000000
 827       6.588920
 828       4.287530
 829     444.607594
 830      74.422733
 831      19.579130
 832       0.000000
 833       0.429668
 834       2.160638
 835      10.288591
 836       0.000000
 837       0.000000
 838      29.900135
 839       0.000000
 840       0.000000
 841     203.972555
 842       0.000000
 843      24.228571
 844       0.000000
 845       0.029187
 846       0.000000
 Length: 847, dtype: float64, Eco_Id
 0         0.024970
 1       152.655645
 2      6322.713113
 3       209.602355
 4         0.000000
 5        15.892546
 6       438.408638
 7       464.718229
 8      1266.631201
 9        55.036082
 10      190.894166
 11     1429.419869
 12      574.924644
 13        0.000000
 14     3252.222466
 15      401.556717
 16       81.656173
 17        4.375072
 18       54.193604
 19     4064.490727
 20        0.000000
 21        0.000000
 22       20.516310
 23      313.042194
 24     1462.453630
 25     1481.163131
 26      615.662183
 27       23.848729
 28       75.920598
 29     2440.757126
           ...     
 817     288.361090
 818    2981.817135
 819      52.549493
 820       3.761721
 821       0.000000
 822       0.000000
 823     297.280486
 824       0.505782
 825       0.373548
 826      14.458807
 827      48.678487
 828      95.406644
 829      33.973338
 830       6.118311
 831      11.165334
 832       0.000000
 833       0.181266
 834       0.648191
 835       0.000000
 836       0.000000
 837       0.000000
 838       3.424855
 839       0.000000
 840       0.188888
 841      83.382461
 842       0.000000
 843       3.980682
 844       0.000000
 845       0.116748
 846       0.000000
 Length: 847, dtype: float64, Eco_Id
 0         0.006320
 1       732.583829
 2      1085.212888
 3       105.560930
 4        11.719240
 5        14.003678
 6         6.534546
 7         0.000000
 8        37.917115
 9         3.369556
 10       87.981948
 11      441.674679
 12       46.513189
 13        0.000000
 14      796.398656
 15      510.621505
 16      314.959526
 17        3.229220
 18      859.030001
 19     3040.390184
 20        0.000000
 21        0.000000
 22        0.000000
 23       51.778674
 24       86.589965
 25      198.007313
 26      166.767327
 27        0.000000
 28      409.088250
 29      814.674632
           ...     
 817     327.091020
 818    2543.787258
 819      12.077106
 820       0.337135
 821       0.000000
 822       0.000000
 823     387.442808
 824       7.839626
 825       0.747096
 826      98.724112
 827     134.249245
 828      91.787143
 829       0.000000
 830      78.635341
 831       8.868596
 832       0.000000
 833       0.040281
 834      19.121645
 835       0.000000
 836       0.000000
 837       0.612386
 838       1.731668
 839       0.000000
 840       0.157407
 841      99.043917
 842       0.094498
 843       0.663447
 844       0.000000
 845       0.000000
 846       0.000000
 Length: 847, dtype: float64, Eco_Id
 0         0.006628
 1      7150.814921
 2         2.905955
 3       350.975940
 4       169.928987
 5        70.148655
 6         0.000000
 7         0.000000
 8         9.179933
 9        12.355039
 10       11.864232
 11       11.980911
 12        1.261986
 13        0.000000
 14       63.100484
 15      391.641737
 16     1941.278314
 17       10.833513
 18     4605.111697
 19     6522.772186
 20        0.000000
 21        0.000000
 22        0.000000
 23        0.000000
 24       35.903156
 25      491.639757
 26      115.502447
 27        0.000000
 28     1937.039563
 29      219.257874
           ...     
 817     116.203040
 818     203.652562
 819      18.869443
 820       3.939161
 821       0.000000
 822       6.045518
 823     344.062823
 824       0.063223
 825       2.552578
 826      61.722004
 827      73.926546
 828     373.780274
 829     299.482358
 830     141.874604
 831     145.786067
 832       0.000000
 833       0.342392
 834      14.692338
 835       0.000000
 836       0.190378
 837       7.924995
 838       0.230889
 839       0.000000
 840       0.000000
 841     131.954768
 842       0.031499
 843       0.000000
 844       0.000000
 845       0.000000
 846       0.000000
 Length: 847, dtype: float64, Eco_Id
 0          0.082770
 1      11923.548771
 2          0.000000
 3        756.564690
 4        175.788607
 5        537.220153
 6          0.000000
 7          0.000000
 8          9.778624
 9         26.956448
 10       187.028292
 11         0.000000
 12         0.360567
 13         0.000000
 14         0.000000
 15      2632.427369
 16      6725.941240
 17         5.416756
 18      7212.075414
 19     11280.511489
 20         0.000000
 21         0.000000
 22         0.000000
 23         0.000000
 24        27.167362
 25      5340.466799
 26       420.332182
 27         0.000000
 28      5320.591210
 29       511.729814
            ...     
 817     1046.516362
 818      125.829995
 819      110.201522
 820       17.158416
 821        0.000000
 822        1.025774
 823        9.356467
 824        0.379337
 825        5.727737
 826      105.564837
 827      136.720090
 828     6976.417993
 829      566.099204
 830      134.803441
 831      221.442009
 832        0.000000
 833        0.000000
 834       22.146538
 835        0.000000
 836        0.571135
 837        0.972613
 838        0.692667
 839        0.000000
 840        0.409258
 841      127.440035
 842        0.031499
 843        0.051034
 844        0.000000
 845        0.000000
 846        0.000000
 Length: 847, dtype: float64, Eco_Id
 0          0.200220
 1      10721.732509
 2          0.968652
 3       1837.171343
 4        410.173416
 5       1365.912201
 6          0.000000
 7          0.000000
 8         23.748088
 9        356.049757
 10       803.168540
 11         0.000000
 12         1.261986
 13         0.000000
 14         0.000000
 15        29.744942
 16      6504.303055
 17        18.125300
 18     12130.890322
 19     14094.587191
 20        11.687516
 21         0.000000
 22         0.000000
 23         0.000000
 24       107.205480
 25     12639.452589
 26      1621.699878
 27         0.000000
 28     11275.273149
 29      3349.207256
            ...     
 817      976.768038
 818      174.443832
 819       76.620872
 820       21.736360
 821        0.000000
 822        0.240075
 823        0.000000
 824        0.316114
 825        0.000000
 826      197.500047
 827       97.640978
 828     8134.293910
 829     1429.834390
 830       82.647348
 831      189.105745
 832        0.000000
 833        0.792200
 834      100.577694
 835        0.000000
 836        0.761513
 837        0.576363
 838       40.059254
 839        0.000000
 840        0.062963
 841       22.355716
 842        0.421303
 843        3.138614
 844        0.000000
 845        0.000000
 846        0.000000
 Length: 847, dtype: float64, Eco_Id
 0          0.214555
 1       9745.226184
 2          0.000000
 3        607.355224
 4        884.802654
 5        354.260477
 6          0.000000
 7          0.000000
 8         97.187552
 9       1590.430461
 10       746.380192
 11         0.000000
 12         2.163404
 13         0.000000
 14         0.000000
 15       252.832007
 16      6145.599150
 17       243.233194
 18     19894.489421
 19     21218.908406
 20       151.937711
 21         0.000000
 22         0.000000
 23         0.000000
 24        64.126493
 25      9477.750497
 26      2206.551925
 27         0.000000
 28     23882.586204
 29      2258.106286
            ...     
 817      761.944790
 818      274.097145
 819       46.486090
 820        4.897335
 821        0.000000
 822        0.000000
 823        0.000000
 824        1.928295
 825        0.000000
 826       91.106030
 827      302.323506
 828     8604.804739
 829      755.168215
 830       49.799039
 831      121.886339
 832        0.000000
 833        0.147698
 834       42.132439
 835        0.000000
 836        0.000000
 837        0.576363
 838       41.406106
 839        0.000000
 840        0.062963
 841       11.208994
 842        0.007875
 843        0.472068
 844        0.000000
 845        0.000000
 846        0.000000
 Length: 847, dtype: float64, Eco_Id
 0          0.117142
 1       6354.230008
 2          0.000000
 3        283.730380
 4       1441.466576
 5         12.896410
 6          0.000000
 7          0.000000
 8        346.442692
 9       6681.829668
 10       178.496710
 11         0.000000
 12         0.360567
 13         0.000000
 14         0.000000
 15        14.872471
 16      2232.907505
 17      1586.172097
 18     29180.770935
 19     19409.859740
 20       451.138126
 21         0.000000
 22         0.000000
 23         0.000000
 24        98.805678
 25      9071.985186
 26       392.793667
 27         0.000000
 28     39722.792272
 29       110.589751
            ...     
 817      786.762155
 818      539.502419
 819       15.125374
 820        6.618500
 821        0.000000
 822        0.000000
 823        0.000000
 824        1.390901
 825        0.373548
 826       73.071390
 827      219.820004
 828     3377.613810
 829      408.049329
 830       32.196358
 831       68.401886
 832        0.000000
 833        0.268542
 834       15.124465
 835        0.611203
 836        0.190378
 837        0.432272
 838       23.396759
 839        0.000000
 840        0.000000
 841       34.810154
 842        0.645735
 843        2.564478
 844        0.000000
 845        0.000000
 846        0.000000
 Length: 847, dtype: float64, Eco_Id
 0          0.038379
 1       4613.792384
 2        135.288367
 3         15.016288
 4        269.542530
 5          0.000000
 6          1.188099
 7          0.236498
 8        288.569201
 9      14162.244123
 10         5.865463
 11        72.014992
 12         0.901418
 13         0.000000
 14       126.592897
 15        39.659923
 16       193.447363
 17      4783.829210
 18     28677.419342
 19      8226.549771
 20       553.988268
 21         0.000000
 22         0.000000
 23         0.729277
 24       310.336707
 25      3618.701261
 26         9.188988
 27         0.000000
 28     34454.470383
 29        14.027892
            ...     
 817      436.669029
 818     1271.438822
 819       13.137373
 820        3.939161
 821        0.000000
 822        0.378300
 823       73.575857
 824        0.126446
 825        0.124516
 826       53.171096
 827      100.140224
 828      917.191252
 829      237.813364
 830       27.231499
 831       20.352290
 832        0.000000
 833        0.966753
 834        6.481914
 835        0.050934
 836        0.190378
 837        0.072045
 838        5.579819
 839        0.000000
 840        0.031481
 841       84.534497
 842        0.023624
 843        0.880343
 844        0.000000
 845        0.218902
 846        0.000000
 Length: 847, dtype: float64, Eco_Id
 0          0.008632
 1         85.307566
 2      21307.756296
 3          6.584523
 4       2642.688722
 5          0.130267
 6        367.716731
 7         51.793024
 8        726.412096
 9      10281.638727
 10         0.799836
 11      1226.197707
 12         1.261986
 13         0.000000
 14      6363.742611
 15      1442.629688
 16       223.582380
 17      5495.924335
 18     10788.970174
 19      1614.645626
 20       112.200156
 21         0.000000
 22       371.003279
 23       586.156467
 24      2746.543451
 25       387.803525
 26       410.261279
 27         0.000000
 28     15965.636673
 29        18.447639
            ...     
 817       36.662920
 818      344.440660
 819       22.265611
 820        2.679339
 821        0.000000
 822        2.036998
 823      380.212811
 824        3.066305
 825        0.000000
 826        0.725532
 827        5.424499
 828       30.462108
 829       76.440010
 830       17.903582
 831        5.048277
 832        0.000000
 833        0.140985
 834      254.955271
 835        0.000000
 836        0.000000
 837        0.000000
 838        0.769630
 839        0.000000
 840        0.000000
 841      199.706910
 842        0.015750
 843        0.127586
 844        0.000000
 845        0.000000
 846        0.000000
 Length: 847, dtype: float64, Eco_Id
 0          0.004932
 1        337.393466
 2      26709.281558
 3         23.745995
 4         76.175063
 5          8.076540
 6       2789.063086
 7       1700.182873
 8       2594.528476
 9       1954.342515
 10        22.928629
 11      4011.144364
 12         3.064822
 13         0.000000
 14     25922.933040
 15        39.659923
 16        28.190822
 17      1924.406864
 18      1534.842361
 19      1668.931758
 20         0.000000
 21         0.000000
 22      2621.813498
 23      4979.504019
 24     10304.565824
 25       682.462356
 26      1669.010363
 27         0.000000
 28       747.459182
 29       147.965436
            ...     
 817        1.232256
 818       26.884160
 819       11.348172
 820       17.672991
 821        0.000000
 822        0.632924
 823      577.123920
 824        0.000000
 825        0.000000
 826        0.000000
 827        1.334824
 828        0.412963
 829       25.110728
 830        6.469362
 831        4.229638
 832        0.000000
 833        0.000000
 834        2.160638
 835        0.000000
 836        0.000000
 837        0.144091
 838        5.271967
 839        0.000000
 840        0.000000
 841      147.211455
 842        0.000000
 843        0.216896
 844        0.000000
 845        0.000000
 846        0.000000
 Length: 847, dtype: float64]

dict_monthly = dict(zip(months,monthly))

dict_monthly['Jan']

Eco_Id
0          0.023428
1       2317.345349
2       8149.267504
3         59.320297
4          0.000000
5         15.762279
6       7294.335597
7       6441.491295
8       3084.058386
9        139.274984
10        33.726413
11      7769.135310
12        66.164109
13         0.000000
14     11038.273505
15       287.534440
16        51.521157
17       293.546527
18        52.556688
19      1268.754934
20         0.000000
21         0.000000
22      2939.816309
23     11913.288339
24      6395.825687
25      1857.676390
26      1981.720437
27         0.000000
28        64.252635
29       479.542527
           ...     
817        5.180774
818       78.327908
819       39.677186
820       17.832687
821        0.000000
822        0.000000
823      409.132801
824        0.000000
825        0.000000
826        0.000000
827        0.000000
828        0.000000
829       34.711889
830       34.453112
831       10.005595
832        0.000000
833        0.120844
834        4.321276
835        0.000000
836        0.000000
837        0.000000
838       10.967231
839        0.000000
840        0.000000
841      173.895088
842        0.003937
843        3.215166
844        0.000000
845        0.000000
846        0.000000
Length: 847, dtype: float64

Por Bioma¶

monthly_biome = [ df_finall[n: 12*17:12].sum() for n in range(12)]

monthly_biome = [mb.groupby(biome_nums).sum()/areas_biome for mb in monthly_biome]

RESULTADO¶

monthly_biome

[0.0         0.023428
 1.0       806.170362
 2.0      4464.016780
 3.0        94.720738
 4.0        98.663361
 5.0        13.905895
 6.0         0.156568
 7.0     13382.845022
 8.0       337.849452
 9.0     20120.968649
 10.0      199.659802
 11.0        0.000000
 12.0      446.268071
 13.0      421.769032
 14.0      611.059681
 dtype: float64, 0.0         0.017571
 1.0      1351.098356
 2.0      3039.033593
 3.0       232.351432
 4.0       113.069964
 5.0        33.090546
 6.0         0.274291
 7.0      5941.738253
 8.0       172.039626
 9.0     12349.343812
 10.0      119.518867
 11.0        0.000000
 12.0      347.631642
 13.0      175.038796
 14.0     1724.630552
 dtype: float64, 0.0        0.024970
 1.0     2169.837995
 2.0     1918.588391
 3.0     1156.991232
 4.0      463.513904
 5.0      237.714845
 6.0      141.921417
 7.0     2670.725290
 8.0      536.600621
 9.0     8232.005701
 10.0      65.673017
 11.0       0.432935
 12.0     347.604322
 13.0     169.724032
 14.0    2538.617713
 dtype: float64, 0.0        0.006320
 1.0      989.661960
 2.0     1450.297921
 3.0     3062.116878
 4.0      823.148837
 5.0      317.206315
 6.0      169.486078
 7.0     2242.725916
 8.0     1250.841515
 9.0     6832.633484
 10.0      41.762032
 11.0       0.256350
 12.0     472.962500
 13.0     304.549687
 14.0    1943.209546
 dtype: float64, 0.0        0.006628
 1.0      424.426734
 2.0     1045.702389
 3.0     4307.335894
 4.0      465.541044
 5.0      297.489068
 6.0      191.173300
 7.0     5807.769091
 8.0      699.840424
 9.0     4055.726718
 10.0     267.962964
 11.0       4.030362
 12.0     223.384467
 13.0     351.173013
 14.0    1329.655744
 dtype: float64, 0.0         0.082770
 1.0       439.594524
 2.0       505.398977
 3.0      1337.399034
 4.0       590.088674
 5.0       223.062867
 6.0       320.103550
 7.0     10116.740886
 8.0       830.667074
 9.0      9863.590182
 10.0      443.117877
 11.0       39.181902
 12.0      363.662262
 13.0      487.625324
 14.0      422.663167
 dtype: float64, 0.0         0.200220
 1.0       799.173001
 2.0       662.309972
 3.0        39.788024
 4.0       230.270337
 5.0       315.574821
 6.0       690.642954
 7.0     13816.263759
 8.0      1425.689808
 9.0     12668.120747
 10.0      533.465470
 11.0       83.145156
 12.0      478.414307
 13.0      645.083528
 14.0      360.382432
 dtype: float64, 0.0         0.214555
 1.0      1590.655214
 2.0      1661.586223
 3.0        15.692689
 4.0       498.371156
 5.0       564.002239
 6.0       350.269219
 7.0     17947.039070
 8.0      1871.647079
 9.0     13289.007531
 10.0      800.622421
 11.0       82.119756
 12.0      638.653731
 13.0     1033.269412
 14.0      472.907057
 dtype: float64, 0.0         0.117142
 1.0      2064.521677
 2.0      2393.454394
 3.0         5.330532
 4.0       517.016136
 5.0       275.463911
 6.0        94.390194
 7.0     17672.101896
 8.0      1462.682274
 9.0      9077.699260
 10.0      622.992203
 11.0        9.766379
 12.0      454.360788
 13.0     1496.675312
 14.0      535.079352
 dtype: float64, 0.0         0.038379
 1.0      1258.992673
 2.0      1526.483621
 3.0         3.437446
 4.0       420.348771
 5.0        72.295821
 6.0        15.445517
 7.0     12094.693021
 8.0       847.861190
 9.0      6862.141799
 10.0      296.449128
 11.0        0.315546
 12.0      439.820611
 13.0     1986.376150
 14.0      521.560493
 dtype: float64, 0.0         0.008632
 1.0       755.936134
 2.0       942.285238
 3.0        12.620575
 4.0       162.728312
 5.0        28.414049
 6.0         0.742013
 7.0     13192.053837
 8.0       134.414667
 9.0      8729.243551
 10.0       69.189775
 11.0        0.000000
 12.0      369.660464
 13.0     1473.536842
 14.0      183.589004
 dtype: float64, 0.0         0.004932
 1.0       748.146110
 2.0       881.479132
 3.0        24.410849
 4.0        74.074689
 5.0        11.020444
 6.0         0.020215
 7.0     17611.936057
 8.0       134.546506
 9.0     18756.852998
 10.0       83.021367
 11.0        0.000000
 12.0      344.250064
 13.0      448.362941
 14.0      295.643722
 dtype: float64]

dict_monthly_biome = dict(zip(months,monthly_biome))

SOMAS ANUAIS¶

Por Ecoregião¶

annual = [ df_finall[n*12: (n+1) *12].sum()/areas for n in range(17)]

annual

[Eco_Id
 0         0.070748
 1      4077.293507
 2      6299.788354
 3        40.162612
 4        76.175063
 5         1.628335
 6       316.034411
 7       542.762511
 8       441.035915
 9      2912.419622
 10       51.856027
 11     2103.200419
 12       89.060136
 13        0.000000
 14     2086.627189
 15        0.000000
 16     1097.497855
 17      664.073496
 18     7336.714856
 19     2899.539680
 20        0.000000
 21        0.000000
 22       57.274700
 23      265.456863
 24      926.138234
 25     2024.892069
 26      268.329823
 27        0.000000
 28     5941.673737
 29      252.213812
           ...     
 817     265.173482
 818     564.466281
 819      19.548676
 820       2.076044
 821       0.000000
 822       0.000000
 823     213.072280
 824       0.000000
 825       0.000000
 826      36.276576
 827      17.523119
 828     927.308850
 829      30.649859
 830      27.432099
 831      38.248661
 832       0.000000
 833       0.006714
 834       0.000000
 835       0.000000
 836       0.190378
 837       0.684431
 838       0.000000
 839       0.000000
 840       0.000000
 841      24.255018
 842       0.011812
 843       0.140345
 844       0.000000
 845       0.000000
 846       0.000000
 Length: 847, dtype: float64, Eco_Id
 0         0.034064
 1      4314.685280
 2      5035.052001
 3       113.962901
 4         0.000000
 5         7.034406
 6       772.858589
 7       677.802770
 8       899.034750
 9       719.961812
 10      103.712054
 11     1441.141733
 12      104.023681
 13        0.000000
 14     4870.495130
 15       79.319845
 16     1390.099143
 17      296.567411
 18     4667.548340
 19     5046.776275
 20       37.400052
 21        0.000000
 22      367.583894
 23      941.496732
 24     1475.077333
 25     1541.206817
 26      288.357834
 27        0.000000
 28     6633.158231
 29      163.050224
           ...     
 817     433.793765
 818     129.266316
 819      14.810607
 820       4.577944
 821       0.000000
 822       0.000000
 823     195.635227
 824       0.316114
 825       0.000000
 826     284.097417
 827     107.950366
 828    4627.714125
 829      77.547836
 830      19.508385
 831      76.110742
 832       0.000000
 833       0.268542
 834       0.000000
 835       0.000000
 836       0.000000
 837       2.413521
 838       0.000000
 839       0.000000
 840       0.094444
 841      79.583858
 842       0.011812
 843       0.051034
 844       0.000000
 845       0.218902
 846       0.000000
 Length: 847, dtype: float64, Eco_Id
 0          0.027436
 1       3863.983774
 2       8275.838005
 3        158.028556
 4        328.138733
 5        112.485355
 6       1359.779639
 7        559.317359
 8        872.891897
 9       4573.610760
 10       170.498351
 11      1187.275936
 12        72.834605
 13         0.000000
 14      3770.743836
 15       198.299613
 16      1477.587901
 17       751.887450
 18     10331.802989
 19      4032.579284
 20        98.175136
 21         0.000000
 22      1549.836280
 23      2636.336706
 24      1545.179686
 25      4373.096296
 26       745.872700
 27         0.000000
 28      8097.566447
 29       474.161966
            ...     
 817      389.724061
 818     1289.732178
 819       52.019359
 820        1.401773
 821        0.000000
 822        0.000000
 823      375.109283
 824        0.221280
 825        4.544834
 826       24.875367
 827       86.507976
 828     1356.280440
 829      533.972244
 830       12.236622
 831       83.887818
 832        0.000000
 833        0.201407
 834       83.076527
 835        0.000000
 836        0.190378
 837        0.252159
 838        0.000000
 839        0.000000
 840        0.000000
 841       38.359668
 842        0.015750
 843        3.240683
 844        0.000000
 845        0.000000
 846        0.000000
 Length: 847, dtype: float64, Eco_Id
 0         0.023120
 1      4689.140598
 2      4698.929830
 3       291.893997
 4         5.859620
 5       135.607706
 6      1283.147234
 7      1164.278799
 8       373.383800
 9      1001.881335
 10      172.897858
 11     1169.531235
 12       95.189781
 13        0.000000
 14     2206.165373
 15        0.000000
 16     1494.113555
 17      442.819832
 18     5472.267816
 19     6865.728492
 20       98.175136
 21        0.000000
 22      173.533792
 23     1235.030764
 24     1606.258250
 25     2548.691977
 26      630.228009
 27        0.000000
 28     6427.628636
 29      706.775161
           ...     
 817     723.188360
 818     388.809630
 819      84.274675
 820       4.435992
 821       0.000000
 822       0.000000
 823     220.727571
 824       0.442560
 825       0.000000
 826       1.399239
 827      59.499084
 828    3210.594490
 829     393.278312
 830      23.971743
 831      70.903284
 832       0.000000
 833       0.053708
 834      53.583820
 835      10.288591
 836       0.000000
 837       0.432272
 838       0.000000
 839       0.000000
 840       0.031481
 841     109.100875
 842       0.645735
 843       8.203776
 844       0.000000
 845       0.000000
 846       0.000000
 Length: 847, dtype: float64, Eco_Id
 0         0.033293
 1      3795.819355
 2      7200.957403
 3       278.069478
 4      2648.548343
 5       181.396475
 6      1737.595213
 7      1166.643777
 8      1286.986704
 9      5208.210484
 10      188.627964
 11     1374.566723
 12       96.812334
 13        0.000000
 14     2781.516371
 15     1814.441463
 16     1757.551924
 17     1089.288870
 18     9556.372614
 19     7701.294765
 20        0.000000
 21        0.000000
 22      276.970190
 23     1936.048373
 24     1382.223515
 25     4355.391106
 26      993.577087
 27        0.000000
 28     9130.260026
 29      896.632111
           ...     
 817     416.051933
 818     540.816306
 819      75.047037
 820      16.182498
 821       0.000000
 822       0.000000
 823     301.958720
 824       0.000000
 825       0.000000
 826       9.535557
 827      58.221060
 828    3314.551901
 829     255.538584
 830      41.624575
 831      93.188473
 832       0.000000
 833       0.134271
 834      35.002334
 835       0.000000
 836       0.380756
 837       6.700223
 838       0.885075
 839       0.000000
 840       0.000000
 841      95.587810
 842       0.000000
 843      11.801700
 844       0.000000
 845       0.116748
 846       0.000000
 Length: 847, dtype: float64, Eco_Id
 0         0.058879
 1      3453.119676
 2      5005.023796
 3       266.896645
 4       275.402151
 5       265.613941
 6       561.376915
 7       593.136548
 8       252.049032
 9       429.056805
 10       79.583671
 11     1338.688751
 12       89.420703
 13        0.000000
 14     4180.309090
 15       29.744942
 16     1607.848939
 17      523.446167
 18     6511.943162
 19     3665.781096
 20      121.550169
 21        0.000000
 22      247.905417
 23      695.912670
 24     2121.406143
 25     1437.969311
 26      887.178280
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 28     7498.479608
 29      799.109437
           ...     
 817     298.868388
 818     935.892169
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 820       4.045625
 821       0.000000
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 825       0.000000
 826      10.364736
 827      62.111931
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 830      15.646828
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 841     109.349964
 842       0.000000
 843       0.791033
 844       0.000000
 845       0.000000
 846       0.000000
 Length: 847, dtype: float64, Eco_Id
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 2      4483.889133
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 29      588.594975
           ...     
 817     673.937881
 818     255.500584
 819      21.039677
 820       5.660326
 821       0.000000
 822       0.000000
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 824       0.252891
 825       1.245160
 826      58.146170
 827      81.765089
 828     796.047284
 829     353.396568
 830      14.092175
 831      92.369834
 832       0.000000
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 834      11.775477
 835       0.000000
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 838      14.507530
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 840       0.377777
 841      89.547408
 842       0.393741
 843       0.280689
 844       0.000000
 845       0.000000
 846       0.000000
 Length: 847, dtype: float64, Eco_Id
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 1      3062.500819
 2      5067.986162
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 26      812.329281
 27        0.000000
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 29      686.501975
           ...     
 817      84.601643
 818     507.463778
 819      14.313607
 820      17.122928
 821       0.000000
 822       0.000000
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 828    1223.476335
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 832       0.000000
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 840       0.000000
 841      70.274165
 842       0.062999
 843       0.000000
 844       0.000000
 845       0.000000
 846       0.000000
 Length: 847, dtype: float64, Eco_Id
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 29      458.885015
           ...     
 817     407.479143
 818     450.259138
 819      31.758316
 820      10.256013
 821       0.000000
 822       0.000000
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 824       0.126446
 825       0.373548
 826      10.727502
 827       6.276514
 828     701.806655
 829     485.597165
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 831      35.246982
 832       0.000000
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 835       0.000000
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 838      23.627648
 839       0.000000
 840       0.000000
 841      43.746213
 842       0.027562
 843       0.089310
 844       0.000000
 845       0.029187
 846       0.000000
 Length: 847, dtype: float64, Eco_Id
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 1      3265.932834
 2      4789.337331
 3       306.999668
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 23     1176.688596
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 26      621.238163
 27        0.000000
 28     8935.767694
 29     1032.491284
           ...     
 817      68.741319
 818      51.140544
 819       9.741205
 820       8.871984
 821       0.000000
 822       0.000000
 823      19.138229
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 828    2132.213987
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 840       0.000000
 841      56.605420
 842       0.007875
 843       0.510344
 844       0.000000
 845       0.000000
 846       0.000000
 Length: 847, dtype: float64, Eco_Id
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 1      2645.187633
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 27        0.000000
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           ...     
 817     150.520699
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 819      11.530406
 820       4.932823
 821       0.000000
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 841     130.833869
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 Length: 847, dtype: float64, Eco_Id
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 29      942.751207
           ...     
 817     116.216290
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 820       2.271228
 821       0.000000
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 840       0.000000
 841      39.293751
 842       0.007875
 843       0.038276
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 846       0.000000
 Length: 847, dtype: float64, Eco_Id
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 29     1115.889984
           ...     
 817     140.039901
 818     729.915037
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 820       4.542456
 821       0.000000
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 841     102.749112
 842       0.000000
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 846       0.000000
 Length: 847, dtype: float64, Eco_Id
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           ...     
 817     115.752538
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 820       3.140682
 821       0.000000
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 Length: 847, dtype: float64, Eco_Id
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           ...     
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 Length: 847, dtype: float64, Eco_Id
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           ...     
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 Length: 847, dtype: float64, Eco_Id
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 Length: 847, dtype: float64]

years = [str(y) for y in range(2001,2018)]

years

['2001',
 '2002',
 '2003',
 '2004',
 '2005',
 '2006',
 '2007',
 '2008',
 '2009',
 '2010',
 '2011',
 '2012',
 '2013',
 '2014',
 '2015',
 '2016',
 '2017']

dict_annual = dict(zip(years,annual))

dict_annual['2002']

Eco_Id
0         0.034064
1      4314.685280
2      5035.052001
3       113.962901
4         0.000000
5         7.034406
6       772.858589
7       677.802770
8       899.034750
9       719.961812
10      103.712054
11     1441.141733
12      104.023681
13        0.000000
14     4870.495130
15       79.319845
16     1390.099143
17      296.567411
18     4667.548340
19     5046.776275
20       37.400052
21        0.000000
22      367.583894
23      941.496732
24     1475.077333
25     1541.206817
26      288.357834
27        0.000000
28     6633.158231
29      163.050224
          ...     
817     433.793765
818     129.266316
819      14.810607
820       4.577944
821       0.000000
822       0.000000
823     195.635227
824       0.316114
825       0.000000
826     284.097417
827     107.950366
828    4627.714125
829      77.547836
830      19.508385
831      76.110742
832       0.000000
833       0.268542
834       0.000000
835       0.000000
836       0.000000
837       2.413521
838       0.000000
839       0.000000
840       0.094444
841      79.583858
842       0.011812
843       0.051034
844       0.000000
845       0.218902
846       0.000000
Length: 847, dtype: float64

Por bioma¶

annual_biome = [ df_finall[n*12: (n+1) *12].sum() for n in range(17)]

annual_biome = [ ab.groupby(biome_nums).sum()/areas_biome for ab in annual_biome]

dict_annual_biome = dict(zip(years,annual_biome))

Gráficos totais por ano e por meses¶

sem áreas¶

annual_total = [ df_finall[n*12: (n+1) *12].sum().sum() for n in range(17)]

fig, ax = plt.subplots()

ax.plot(years, annual_total)

labels = ax.get_xticklabels()

plt.tight_layout()

plt.setp(labels, rotation=30, fontsize=10);plt.show()

monthly_total = [ df_finall[n: 12*17:12].sum().sum() for n in range(12)]

fig, ax = plt.subplots()
#ax[0].plot( np.arange(1,13),monthly_total)
#labels = ax.get_xticklabels()
#plt.setp(labels, rotation=30, fontsize=10)
#plt.xticks(months,months)
ax.plot( np.arange(12),monthly_total)
ax.set_xticks(np.arange(12))
ax.set_xticklabels(months)
plt.show()

months

['Jan',
 'Fev',
 'Mar',
 'Abr',
 'Mai',
 'Jun',
 'Jul',
 'Ago',
 'Set',
 'Out',
 'Nov',
 'Dez']

all_months = [df_finall.iloc[m].sum() for m in range(17*12)]

import itertools
ym = itertools.product(years,months)
ymlabels = [y + m for y, m in ym]
ymlabels

from statistics import mean
media = mean(all_months)

fig, ax = plt.subplots()
ax.plot( np.arange(12*17),all_months)
ax.axhline(y=media, color='r', linestyle='--')
ax.set_xticks(np.arange(12*17))
ax.set_xticklabels(ymlabels)
plt.setp(labels, rotation=60);
plt.show()

['2001Jan',
 '2001Fev',
 '2001Mar',
 '2001Abr',
 '2001Mai',
 '2001Jun',
 '2001Jul',
 '2001Ago',
 '2001Set',
 '2001Out',
 '2001Nov',
 '2001Dez',
 '2002Jan',
 '2002Fev',
 '2002Mar',
 '2002Abr',
 '2002Mai',
 '2002Jun',
 '2002Jul',
 '2002Ago',
 '2002Set',
 '2002Out',
 '2002Nov',
 '2002Dez',
 '2003Jan',
 '2003Fev',
 '2003Mar',
 '2003Abr',
 '2003Mai',
 '2003Jun',
 '2003Jul',
 '2003Ago',
 '2003Set',
 '2003Out',
 '2003Nov',
 '2003Dez',
 '2004Jan',
 '2004Fev',
 '2004Mar',
 '2004Abr',
 '2004Mai',
 '2004Jun',
 '2004Jul',
 '2004Ago',
 '2004Set',
 '2004Out',
 '2004Nov',
 '2004Dez',
 '2005Jan',
 '2005Fev',
 '2005Mar',
 '2005Abr',
 '2005Mai',
 '2005Jun',
 '2005Jul',
 '2005Ago',
 '2005Set',
 '2005Out',
 '2005Nov',
 '2005Dez',
 '2006Jan',
 '2006Fev',
 '2006Mar',
 '2006Abr',
 '2006Mai',
 '2006Jun',
 '2006Jul',
 '2006Ago',
 '2006Set',
 '2006Out',
 '2006Nov',
 '2006Dez',
 '2007Jan',
 '2007Fev',
 '2007Mar',
 '2007Abr',
 '2007Mai',
 '2007Jun',
 '2007Jul',
 '2007Ago',
 '2007Set',
 '2007Out',
 '2007Nov',
 '2007Dez',
 '2008Jan',
 '2008Fev',
 '2008Mar',
 '2008Abr',
 '2008Mai',
 '2008Jun',
 '2008Jul',
 '2008Ago',
 '2008Set',
 '2008Out',
 '2008Nov',
 '2008Dez',
 '2009Jan',
 '2009Fev',
 '2009Mar',
 '2009Abr',
 '2009Mai',
 '2009Jun',
 '2009Jul',
 '2009Ago',
 '2009Set',
 '2009Out',
 '2009Nov',
 '2009Dez',
 '2010Jan',
 '2010Fev',
 '2010Mar',
 '2010Abr',
 '2010Mai',
 '2010Jun',
 '2010Jul',
 '2010Ago',
 '2010Set',
 '2010Out',
 '2010Nov',
 '2010Dez',
 '2011Jan',
 '2011Fev',
 '2011Mar',
 '2011Abr',
 '2011Mai',
 '2011Jun',
 '2011Jul',
 '2011Ago',
 '2011Set',
 '2011Out',
 '2011Nov',
 '2011Dez',
 '2012Jan',
 '2012Fev',
 '2012Mar',
 '2012Abr',
 '2012Mai',
 '2012Jun',
 '2012Jul',
 '2012Ago',
 '2012Set',
 '2012Out',
 '2012Nov',
 '2012Dez',
 '2013Jan',
 '2013Fev',
 '2013Mar',
 '2013Abr',
 '2013Mai',
 '2013Jun',
 '2013Jul',
 '2013Ago',
 '2013Set',
 '2013Out',
 '2013Nov',
 '2013Dez',
 '2014Jan',
 '2014Fev',
 '2014Mar',
 '2014Abr',
 '2014Mai',
 '2014Jun',
 '2014Jul',
 '2014Ago',
 '2014Set',
 '2014Out',
 '2014Nov',
 '2014Dez',
 '2015Jan',
 '2015Fev',
 '2015Mar',
 '2015Abr',
 '2015Mai',
 '2015Jun',
 '2015Jul',
 '2015Ago',
 '2015Set',
 '2015Out',
 '2015Nov',
 '2015Dez',
 '2016Jan',
 '2016Fev',
 '2016Mar',
 '2016Abr',
 '2016Mai',
 '2016Jun',
 '2016Jul',
 '2016Ago',
 '2016Set',
 '2016Out',
 '2016Nov',
 '2016Dez',
 '2017Jan',
 '2017Fev',
 '2017Mar',
 '2017Abr',
 '2017Mai',
 '2017Jun',
 '2017Jul',
 '2017Ago',
 '2017Set',
 '2017Out',
 '2017Nov',
 '2017Dez']

df_finall

annual_total

[20024630.0,
 21610924.0,
 20411080.0,
 21490071.0,
 21322025.0,
 20108210.0,
 21651364.0,
 19752869.0,
 17812399.0,
 19432613.0,
 21420223.0,
 21207740.0,
 16579315.0,
 17711851.0,
 18036370.0,
 17067128.0,
 15889270.0]

n = 0

df_finall[n*12: (n+1) *12].sum().sum()

20024630.0

ar_annual_biome = np.array(annual_biome)

#testing
lixo =  np.array(annual_biome)

lixo.shape

(17, 15)

ar_annual_biome.reshape(17,15)

array([[  7.07476433e-02,   4.76112748e+02,   9.46678098e+02,
          3.82337005e+02,   1.93003218e+02,   4.90009755e+01,
          6.00031395e+01,   8.07719653e+03,   7.08472528e+02,
          6.66113297e+03,   2.10577024e+02,   7.03733127e+00,
          2.64166778e+02,   8.81544288e+02,   2.80317529e+02],
       [  3.40636801e-02,   6.74108112e+02,   1.07549863e+03,
          4.75613019e+02,   2.59586694e+02,   2.22334763e+02,
          1.47100443e+02,   7.79192727e+03,   8.96827624e+02,
          1.07574762e+04,   2.69159255e+02,   1.12322389e+01,
          3.38886287e+02,   1.12661090e+03,   5.01499084e+02],
       [  2.74359052e-02,   8.76820248e+02,   1.27224495e+03,
          7.72644899e+02,   2.52820795e+02,   2.17116682e+02,
          2.26887020e+02,   8.37276002e+03,   6.79262724e+02,
          6.66430540e+03,   3.03836865e+02,   3.89927750e+01,
          3.70555945e+02,   2.25426578e+02,   7.92552177e+02],
       [  2.31201449e-02,   9.57398018e+02,   1.65313066e+03,
          3.12674751e+02,   2.32042786e+02,   4.63582262e+01,
          8.80367110e+01,   8.75564351e+03,   5.59566440e+02,
          8.35537054e+03,   1.93387755e+02,   4.16661381e+01,
          3.37049793e+02,   4.83547430e+02,   7.73972782e+02],
       [  3.32930086e-02,   9.69626708e+02,   1.29752911e+03,
          9.77911912e+02,   2.59779096e+02,   6.71833599e+01,
          8.97375530e+01,   8.85421422e+03,   6.75703910e+02,
          8.29405271e+03,   2.38218996e+02,   1.17755401e+01,
          3.24088091e+02,   3.10801546e+02,   6.80063699e+02],
       [  5.88793023e-02,   7.66412267e+02,   1.11808828e+03,
          5.68988670e+02,   3.53606805e+02,   1.02089673e+02,
          9.44373630e+01,   7.94101709e+03,   7.10640359e+02,
          7.49075605e+03,   2.30317957e+02,   4.43470263e+00,
          2.44390317e+02,   6.16475587e+02,   8.69942226e+02],
       [  1.01420369e-01,   1.08109566e+03,   1.55381901e+03,
          4.95689697e+02,   2.55561483e+02,   1.54728923e+02,
          5.91901759e+01,   8.75193836e+03,   5.85682205e+02,
          8.36790873e+03,   2.12961123e+02,   8.98077448e+00,
          3.10658917e+02,   4.89046639e+02,   6.24180927e+02],
       [  2.88231139e-02,   6.91240406e+02,   1.17208347e+03,
          6.76462831e+02,   4.06588582e+02,   2.34986700e+02,
          1.20913183e+02,   7.97004971e+03,   8.13971942e+02,
          8.28287819e+03,   2.09747680e+02,   4.44975252e+00,
          1.97324463e+02,   3.41816923e+02,   5.08186220e+02],
       [  2.48156222e-02,   8.13706783e+02,   1.08838375e+03,
          6.14937527e+02,   3.53201238e+02,   8.10061070e+01,
          7.10385961e+01,   7.35347555e+03,   3.97453108e+02,
          9.02833249e+03,   1.86277880e+02,   6.77395809e+00,
          3.17106377e+02,   2.01442633e+02,   7.40103340e+02],
       [  2.18870705e-02,   1.03721008e+03,   1.41128187e+03,
          3.35109484e+02,   2.81539185e+02,   7.98645112e+01,
          8.31708202e+01,   7.81739170e+03,   5.46414268e+02,
          6.29833978e+03,   2.30595819e+02,   2.85566756e+01,
          2.76767253e+02,   3.90489644e+02,   5.61430285e+02],
       [  2.82065767e-02,   6.51342645e+02,   1.18075914e+03,
          1.44102060e+03,   2.67334673e+02,   1.38863439e+02,
          1.17194400e+02,   8.12974831e+03,   4.14446128e+02,
          7.86826552e+03,   2.34267416e+02,   2.95178623e+00,
          1.87046167e+02,   1.21146267e+03,   6.30434303e+02],
       [  8.86272220e-02,   7.77016427e+02,   1.29332884e+03,
          7.61269775e+02,   2.82519880e+02,   2.18647049e+02,
          1.76553431e+02,   8.19587690e+03,   4.52575079e+02,
          7.81030952e+03,   1.90375815e+02,   6.32898284e+00,
          2.84079838e+02,   8.91924271e+02,   5.47441518e+02],
       [  2.06539961e-02,   6.61425510e+02,   9.83462567e+02,
          6.28072889e+02,   1.93003218e+02,   1.48020924e+02,
          1.41622947e+02,   7.10571648e+03,   1.71562336e+02,
          6.68093460e+03,   1.78822268e+02,   5.47414876e+00,
          1.83200154e+02,   2.37551761e+02,   6.17529937e+02],
       [  2.14246676e-02,   7.11453916e+02,   9.20990414e+02,
          2.97181334e+02,   2.96385959e+02,   1.33816147e+02,
          1.71932500e+02,   7.11578132e+03,   5.88948971e+02,
          6.60041175e+03,   1.86477262e+02,   2.04327418e+00,
          2.64922625e+02,   3.71917709e+02,   7.56622374e+02],
       [  2.95937854e-02,   8.57402464e+02,   1.29470753e+03,
          2.20909884e+02,   2.14864352e+02,   1.74392236e+02,
          1.31431768e+02,   7.03525418e+03,   5.55915840e+02,
          8.90650178e+03,   1.76915413e+02,   1.19972753e+01,
          3.36661245e+02,   3.98958064e+02,   9.34283322e+02],
       [  2.01915932e-02,   7.60193479e+02,   1.09996965e+03,
          5.49725686e+02,   1.57682263e+02,   9.10534980e+01,
          9.84716604e+01,   7.29515087e+03,   3.50450071e+02,
          7.23445227e+03,   1.22316578e+02,   1.88239080e+01,
          2.62661157e+02,   1.72461782e+02,   6.60146877e+02],
       [  1.12363904e-01,   6.35649273e+02,   1.12868067e+03,
          7.81645362e+02,   1.97314956e+02,   2.29777608e+02,
          9.69036038e+01,   5.93349007e+03,   5.96786704e+02,
          5.53590589e+03,   1.69179818e+02,   7.72912481e+00,
          4.27107822e+02,   6.41705644e+02,   4.60291865e+02]])

anbio = ar_annual_biome.T

anbio[0]

array([ 0.07074764,  0.03406368,  0.02743591,  0.02312014,  0.03329301,
        0.0588793 ,  0.10142037,  0.02882311,  0.02481562,  0.02188707,
        0.02820658,  0.08862722,  0.020654  ,  0.02142467,  0.02959379,
        0.02019159,  0.1123639 ])

annual_biome

[0.0        0.070748
 1.0      476.112748
 2.0      946.678098
 3.0      382.337005
 4.0      193.003218
 5.0       49.000975
 6.0       60.003140
 7.0     8077.196531
 8.0      708.472528
 9.0     6661.132970
 10.0     210.577024
 11.0       7.037331
 12.0     264.166778
 13.0     881.544288
 14.0     280.317529
 dtype: float64, 0.0         0.034064
 1.0       674.108112
 2.0      1075.498635
 3.0       475.613019
 4.0       259.586694
 5.0       222.334763
 6.0       147.100443
 7.0      7791.927270
 8.0       896.827624
 9.0     10757.476237
 10.0      269.159255
 11.0       11.232239
 12.0      338.886287
 13.0     1126.610899
 14.0      501.499084
 dtype: float64, 0.0        0.027436
 1.0      876.820248
 2.0     1272.244951
 3.0      772.644899
 4.0      252.820795
 5.0      217.116682
 6.0      226.887020
 7.0     8372.760020
 8.0      679.262724
 9.0     6664.305398
 10.0     303.836865
 11.0      38.992775
 12.0     370.555945
 13.0     225.426578
 14.0     792.552177
 dtype: float64, 0.0        0.023120
 1.0      957.398018
 2.0     1653.130656
 3.0      312.674751
 4.0      232.042786
 5.0       46.358226
 6.0       88.036711
 7.0     8755.643506
 8.0      559.566440
 9.0     8355.370538
 10.0     193.387755
 11.0      41.666138
 12.0     337.049793
 13.0     483.547430
 14.0     773.972782
 dtype: float64, 0.0        0.033293
 1.0      969.626708
 2.0     1297.529114
 3.0      977.911912
 4.0      259.779096
 5.0       67.183360
 6.0       89.737553
 7.0     8854.214220
 8.0      675.703910
 9.0     8294.052712
 10.0     238.218996
 11.0      11.775540
 12.0     324.088091
 13.0     310.801546
 14.0     680.063699
 dtype: float64, 0.0        0.058879
 1.0      766.412267
 2.0     1118.088281
 3.0      568.988670
 4.0      353.606805
 5.0      102.089673
 6.0       94.437363
 7.0     7941.017089
 8.0      710.640359
 9.0     7490.756050
 10.0     230.317957
 11.0       4.434703
 12.0     244.390317
 13.0     616.475587
 14.0     869.942226
 dtype: float64, 0.0        0.101420
 1.0     1081.095656
 2.0     1553.819009
 3.0      495.689697
 4.0      255.561483
 5.0      154.728923
 6.0       59.190176
 7.0     8751.938363
 8.0      585.682205
 9.0     8367.908731
 10.0     212.961123
 11.0       8.980774
 12.0     310.658917
 13.0     489.046639
 14.0     624.180927
 dtype: float64, 0.0        0.028823
 1.0      691.240406
 2.0     1172.083468
 3.0      676.462831
 4.0      406.588582
 5.0      234.986700
 6.0      120.913183
 7.0     7970.049707
 8.0      813.971942
 9.0     8282.878189
 10.0     209.747680
 11.0       4.449753
 12.0     197.324463
 13.0     341.816923
 14.0     508.186220
 dtype: float64, 0.0        0.024816
 1.0      813.706783
 2.0     1088.383745
 3.0      614.937527
 4.0      353.201238
 5.0       81.006107
 6.0       71.038596
 7.0     7353.475552
 8.0      397.453108
 9.0     9028.332491
 10.0     186.277880
 11.0       6.773958
 12.0     317.106377
 13.0     201.442633
 14.0     740.103340
 dtype: float64, 0.0        0.021887
 1.0     1037.210079
 2.0     1411.281867
 3.0      335.109484
 4.0      281.539185
 5.0       79.864511
 6.0       83.170820
 7.0     7817.391705
 8.0      546.414268
 9.0     6298.339781
 10.0     230.595819
 11.0      28.556676
 12.0     276.767253
 13.0     390.489644
 14.0     561.430285
 dtype: float64, 0.0        0.028207
 1.0      651.342645
 2.0     1180.759136
 3.0     1441.020598
 4.0      267.334673
 5.0      138.863439
 6.0      117.194400
 7.0     8129.748315
 8.0      414.446128
 9.0     7868.265518
 10.0     234.267416
 11.0       2.951786
 12.0     187.046167
 13.0    1211.462668
 14.0     630.434303
 dtype: float64, 0.0        0.088627
 1.0      777.016427
 2.0     1293.328843
 3.0      761.269775
 4.0      282.519880
 5.0      218.647049
 6.0      176.553431
 7.0     8195.876899
 8.0      452.575079
 9.0     7810.309519
 10.0     190.375815
 11.0       6.328983
 12.0     284.079838
 13.0     891.924271
 14.0     547.441518
 dtype: float64, 0.0        0.020654
 1.0      661.425510
 2.0      983.462567
 3.0      628.072889
 4.0      193.003218
 5.0      148.020924
 6.0      141.622947
 7.0     7105.716476
 8.0      171.562336
 9.0     6680.934603
 10.0     178.822268
 11.0       5.474149
 12.0     183.200154
 13.0     237.551761
 14.0     617.529937
 dtype: float64, 0.0        0.021425
 1.0      711.453916
 2.0      920.990414
 3.0      297.181334
 4.0      296.385959
 5.0      133.816147
 6.0      171.932500
 7.0     7115.781325
 8.0      588.948971
 9.0     6600.411750
 10.0     186.477262
 11.0       2.043274
 12.0     264.922625
 13.0     371.917709
 14.0     756.622374
 dtype: float64, 0.0        0.029594
 1.0      857.402464
 2.0     1294.707535
 3.0      220.909884
 4.0      214.864352
 5.0      174.392236
 6.0      131.431768
 7.0     7035.254183
 8.0      555.915840
 9.0     8906.501784
 10.0     176.915413
 11.0      11.997275
 12.0     336.661245
 13.0     398.958064
 14.0     934.283322
 dtype: float64, 0.0        0.020192
 1.0      760.193479
 2.0     1099.969645
 3.0      549.725686
 4.0      157.682263
 5.0       91.053498
 6.0       98.471660
 7.0     7295.150872
 8.0      350.450071
 9.0     7234.452273
 10.0     122.316578
 11.0      18.823908
 12.0     262.661157
 13.0     172.461782
 14.0     660.146877
 dtype: float64, 0.0        0.112364
 1.0      635.649273
 2.0     1128.680668
 3.0      781.645362
 4.0      197.314956
 5.0      229.777608
 6.0       96.903604
 7.0     5933.490065
 8.0      596.786704
 9.0     5535.905886
 10.0     169.179818
 11.0       7.729125
 12.0     427.107822
 13.0     641.705644
 14.0     460.291865
 dtype: float64]

fig, ax = plt.subplots(5,3, figsize =(18,12))
for i in range(5):
    for j in range(3):
        ax[i][j].plot(years, anbio[i*3 + j])
        labels = ax[i][j].get_xticklabels()
        plt.setp(labels, rotation=30, fontsize=10) 

plt.show()

basket = 'BasketSemNomes.csv'

basket_df = pd.read_csv(basket,sep=';')

basket_df

basket_df.describe()

!cat ITDcluster.r

# Import/Prepare Data
rm(list=ls())
graphics.off()
#Importing Data
#data(mtcars)
#mydata<-mtcars
#mydata <- read.table("C:/UTAD/InstTecnDesporto/2012_2013/TrabalhoAlunos/Basket_noheader.csv", header=FALSE,sep=";")
mydata <- read.table("C:/UTAD/InstTecnDesporto/2012_2013/TrabalhoAlunos/BasketSemNomes.csv", header=TRUE,sep=";");
mydata
#Sys.sleep(1)
sink(file="summaryBasket.txt")
summary(mydata)
sink(NULL)

mydata <- na.omit(mydata) # listwise deletion of missing
mydata <- scale(mydata) # standardize variables 

#K-means clustering is the most popular partitioning method. It requires the analyst to specify the number of clusters to extract. A plot of the within groups sum of squares by number of clusters extracted can help determine the appropriate number of clusters. The analyst looks for a bend in the plot similar to a scree test in #factor analysis.

# Determine number of clusters
wss <- (nrow(mydata)-1)*sum(apply(mydata,2,var))
for (i in 2:15) wss[i] <- sum(kmeans(mydata,
   centers=i)$withinss)
#jpeg("Plot_NumberOfClusters.jpg")
plot(1:15, wss, type="b", xlab="Number of Clusters",
  ylab="Within groups sum of squares") 
dev.copy(jpeg,filename="Plot_NumberOfClusters.jpg");
dev.off( )
#readline(prompt = "Pause. Press <Enter> to continue...")

# K-Means Cluster Analysis
fit <- kmeans(mydata, 5) # 5 cluster solution
# get cluster means
sink(file="ClusterMeans.txt")
aggregate(mydata,by=list(fit$cluster),FUN=mean)
sink(NULL)
# append cluster assignment
mydata <- data.frame(mydata, fit$cluster) 

#A robust version of K-means based on mediods can be invoked by using pam( ) instead of kmeans( ). The function pamk( ) in the fpc package is a wrapper for pam that also prints the suggested number of clusters based on optimum average silhouette width. 

#Hierarchical Agglomerative
#There are a wide range of hierarchical clustering approaches. I have had good luck with Ward's method described below. 
############################################################################################################
# Ward Hierarchical Clustering
d <- dist(mydata, method = "euclidean") # distance matrix
fit <- hclust(d, method="ward")
#jpeg("Plot_WardHierarchicalClusteringDendogram.jpg")
plot(fit) # display dendogram
dev.copy(jpeg,filename="Plot_WardHierarchicalClusteringDendogram.jpg");
dev.off( )

groups <- cutree(fit, k=5) # cut tree into 5 clusters
# draw dendogram with red borders around the 5 clusters
rect.hclust(fit, k=5, border="red")
dev.copy(jpeg,filename="Plot_WardHierarchicalClusteringDendogram.jpg");
dev.off( )
#The pvclust( ) function in the pvclust package provides p-values for hierarchical clustering based on multiscale bootstrap resampling. Clusters that are highly supported by the data will have large p values. Interpretation details are provided Suzuki. Be aware that pvclust clusters columns, not rows. Transpose your data before using. 
############################################################################################################
# Ward Hierarchical Clustering with Bootstrapped p values
library(pvclust)
fit <- pvclust(mydata, method.hclust="ward",
   method.dist="euclidean")
#jpeg("Plot_WardHierarchicalClusteringDendogram_withBootstrapped_p_values.jpg")
plot(fit) # dendogram with p values
# add rectangles around groups highly supported by the data
pvrect(fit, alpha=.95) 
dev.copy(jpeg,filename="Plot_WardHierarchicalClusteringDendogram_withBootstrapped_p_values.jpg");
dev.off( )
#Model Based

#Model based approaches assume a variety of data models and apply maximum likelihood estimation and Bayes criteria to identify the most likely model and number of clusters. Specifically, the Mclust( ) function in the mclust package selects the optimal model according to BIC for EM initialized by hierarchical clustering for parameterized Gaussian mixture models. (phew!). One chooses the model and number of clusters with the largest BIC. See help(mclustModelNames) to details on the model chosen as best. 

# Model Based Clustering
#data(mtcars)
#mydata<-mtcars
#mydata <- read.table("C:/UTAD/InstTecnDesporto/2012_2013/TrabalhoAlunos/BasketComNomes.csv", header=TRUE,sep=";");
#mydata
library(mclust)
fit <- Mclust(mydata)
#jpeg("Plot_ModelBasedClusteringResults.jpg")
plot(fit) # plot results
dev.copy(jpeg,filename="Plot_ModelBasedClusteringResults.jpg");
dev.off( )

print(fit) # display the best model 

#Plotting Cluster Solutions

#It is always a good idea to look at the cluster results.

# K-Means Clustering with 5 clusters
fit <- kmeans(mydata, 5)

# Cluster Plot against 1st 2 principal components

# vary parameters for most readable graph
library(cluster)
#jpeg("Plot_KMeansClusterplot.jpg")
clusplot(mydata, fit$cluster, color=TRUE, shade=TRUE,
   labels=2, lines=0)
dev.copy(jpeg,filename="Plot_KMeansClusterplot.jpg");
dev.off( )

# Centroid Plot against 1st 2 discriminant functions
library(fpc)
jpeg("Plot_CentroidPlot.jpg")
plotcluster(mydata, fit$cluster) 
dev.off( )

#Validating cluster solutions

#The function cluster.stats() in the fpc package provides a mechanism for comparing the similarity of two cluster solutions using a variety of validation criteria (Hubert's gamma coefficient, the Dunn index and the corrected rand index)

# comparing 2 cluster solutions
#library(fpc)
#cluster.stats(d, fit1$cluster, fit2$cluster)

TODOS OS PIXEIS¶

allpixels = np.load('../MODIS/MCD64A1/allpixels.npy')

allpixels[0]

20029079

annual_total[0]

20024630.0

an_total = np.array(annual_total[:-1])

diffs = (allpixels - an_total)*100/allpixels

diffs.max()

0.062423688066403825

fig, ax = plt.subplots(3,1)
ax[0].plot(years[:-1], annual_total[:-1])
ax[0].set_title("Pixeis havendo selecção p/ ecoregião")
ax[1].plot(years[:-1], allpixels)
ax[1].set_title("Pixeis não havendo selecção p/ ecoregião")
ax[2].plot(years[:-1], diffs)
ax[2].set_title("Diferença em percentagem %")
labels = [ax[i].get_xticklabels() for i in range(3)]
plt.setp(labels, rotation=30, fontsize=10)
fig.tight_layout()
plt.show()

biomes again¶

biome_names_dict = dict( (num, name) for num, name in zip(biome_nums, biome_names))

biome_names_dict

{0.0: 'N/A',
 1.0: 'Tropical & Subtropical Moist Broadleaf Forests',
 2.0: 'Tropical & Subtropical Dry Broadleaf Forests',
 3.0: 'Tropical & Subtropical Coniferous Forests',
 4.0: 'Temperate Broadleaf & Mixed Forests',
 5.0: 'Temperate Conifer Forests',
 6.0: 'Boreal Forests/Taiga',
 7.0: 'Tropical & Subtropical Grasslands, Savannas & Shrublands',
 8.0: 'Temperate Grasslands, Savannas & Shrublands',
 9.0: 'Flooded Grasslands & Savannas',
 10.0: 'Montane Grasslands & Shrublands',
 11.0: 'Tundra',
 12.0: 'Mediterranean Forests, Woodlands & Scrub',
 13.0: 'Deserts & Xeric Shrublands',
 14.0: 'Mangroves'}

fig, ax = plt.subplots(5,3, figsize =(18,12))
for i in range(5):
    for j in range(3):
        ax[i][j].plot(years, anbio[i*3 + j])
        labels = ax[i][j].get_xticklabels()
        ax[i][j].set_title(biome_names_dict[i*3 + j])
        plt.setp(labels, rotation=30, fontsize=10) 
fig.tight_layout()
plt.show()

todos os gráficos com a mesma escala?¶

experimentar aplicar a escala das savanas às florestas mediterrânicas

savanas = 7; mediterraneo = 12

fig, ax = plt.subplots(2,1, sharey=True)
ax[0].plot(years, anbio[savanas])
labels = ax[0].get_xticklabels()
ax[0].set_title(biome_names_dict[savanas])
plt.setp(labels, rotation=30, fontsize=10) 
ax[1].plot(years, anbio[mediterraneo])
labels = ax[1].get_xticklabels()
ax[1].set_title(biome_names_dict[mediterraneo])
plt.setp(labels, rotation=30, fontsize=10)
fig.tight_layout()
plt.show()

fig, ax = plt.subplots(2,1, sharey=True)

ver também sharey='col' e sharey='row'

log_anbio = (np.log(anbio) - np.log(anbio.min()))/(np.log(anbio.max()) - np.log(anbio.min()))

fig, ax = plt.subplots(2,1, sharey=True)
fig.suptitle('escala logarítmica normalizada')
ax[0].plot(years, log_anbio[savanas])
labels = ax[0].get_xticklabels()
ax[0].set_title(biome_names_dict[savanas])
plt.setp(labels, rotation=30, fontsize=10) 
ax[1].plot(years, log_anbio[mediterraneo])
labels = ax[1].get_xticklabels()
ax[1].set_title(biome_names_dict[mediterraneo])
plt.setp(labels, rotation=30, fontsize=10)
fig.tight_layout()
plt.show()

log2_anbio = np.log(anbio -anbio.min() + 1) / np.log(anbio.max() - anbio.min() +1 )

fig, ax = plt.subplots(2,1, sharey=True)
fig.suptitle('escala logarítmica normalizada 2')
ax[0].plot(years, log2_anbio[savanas])
labels = ax[0].get_xticklabels()
ax[0].set_title(biome_names_dict[savanas])
plt.setp(labels, rotation=30, fontsize=10) 
ax[1].plot(years, log2_anbio[mediterraneo])
labels = ax[1].get_xticklabels()
ax[1].set_title(biome_names_dict[mediterraneo])
plt.setp(labels, rotation=30, fontsize=10)
fig.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.show()

Comparar fórmulas de logaritmos¶

x = np.linspace(.1,100000, 1000000)

log1 = np.log(x - x.min() +1) /np.log(x.max() - x.min() +1)

log2 = (np.log(x) - np.log(x.min()))/(np.log(x.max()) - np.log(x.min()))

fig, ax = plt.subplots(2,1, sharey=True)
fig.suptitle('escalas logarítmicas normalizadas')
ax[0].plot(x, log2, 'b')

ax[0].set_title(r"$\frac{\log(x) - \log(x_{min})}  {\log(x_{max}) - \log(x_{min})}$"  )
 
ax[1].plot(x, log1, 'r')

ax[1].set_title(r"$\frac{\log(x - x_{min} +1)}{ \log(x_{max}- x_{min} +1)}$")

fig.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.show()

escala horizontal, ano sim, ano não¶

fig, ax = plt.subplots(2,1, sharey=True,sharex=True )
labels = years[::2]
ax[0].plot(years, anbio[savanas])

ax[0].set_title(biome_names_dict[savanas])



ax[1].plot(years, anbio[mediterraneo])

ax[1].set_title(biome_names_dict[mediterraneo])

plt.xticks(labels,labels)

fig.tight_layout()
plt.show()

Alternativa¶

fig, ax = plt.subplots(2,1, sharey=True)
labels = years[::2]
#plt.setp(ax, xticks=labels, xticklabels=labels)
ax[0].plot(years, anbio[savanas])

ax[0].set_title(biome_names_dict[savanas])

ax[0].set_xticks(labels)
ax[0].set_xticklabels(labels)

ax[1].plot(years, anbio[mediterraneo])

ax[1].set_title(biome_names_dict[mediterraneo])

ax[1].set_xticks(labels)
ax[1].set_xticklabels(labels)

fig.tight_layout()
plt.show()

from scipy import stats

years_float = np.array([float(yr) for yr in years])

fig, ax = plt.subplots(5,3, figsize =(18,12) , sharey=True)
labels = years[::2]
for i in range(5):
    for j in range(3):
        slope, intercept, r_value, p_value, std_err = stats.linregress(years_float, anbio[i*3 + j])
        ax[i][j].plot(years, intercept + slope*years_float, 'r', label=r"$R^2$: {:5.3f} ".format(r_value**2))
        ax[i][j].plot(years, anbio[i*3 + j], 'o')
        ax[i][j].set_xticks(labels)
        ax[i][j].set_xticklabels(labels)
        ax[i][j].set_title(biome_names_dict[i*3 + j])
        ax[i][j].legend()         
fig.tight_layout()
plt.show()

# https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.linregress.html

Test seaborne style¶

import seaborn as sns; sns.set()

fig, ax = plt.subplots(2,1, sharey=True)
labels = years[::2]
#plt.setp(ax, xticks=labels, xticklabels=labels)
ax[0].plot(years, anbio[savanas])

ax[0].set_title(biome_names_dict[savanas])

ax[0].set_xticks(labels)
ax[0].set_xticklabels(labels)

ax[1].plot(years, anbio[mediterraneo])

ax[1].set_title(biome_names_dict[mediterraneo])

ax[1].set_xticks(labels)
ax[1].set_xticklabels(labels)

fig.tight_layout()
plt.show()

fig, ax = plt.subplots(5,3, figsize =(18,12) , sharey=True)
labels = years[::2]
for i in range(5):
    for j in range(3):
        slope, intercept, r_value, p_value, std_err = stats.linregress(years_float, anbio[i*3 + j])
        ax[i][j].plot(years, intercept + slope*years_float, 'r', label="r-squared: {} ".format(r_value**2))
        ax[i][j].plot(years, anbio[i*3 + j], 'o')
        ax[i][j].set_xticks(labels)
        ax[i][j].set_xticklabels(labels)
        ax[i][j].set_title(biome_names_dict[i*3 + j])
        ax[i][j].legend()         
fig.tight_layout()
plt.show()

 %%HTML
    <h1 id="1">PLOTS<h1>

«««

Variação inter-anual. Totais.¶

fig, ax = plt.subplots()
labels = years[::2]
ax.plot(years, annual_total)
ax.set_xticks(labels)
ax.set_xticklabels(labels, fontsize=15)
ax.set_title('Interannual Variability',fontsize=15)
labels_x = ax.get_xticklabels()
labels_y = ax.get_yticklabels()
off =ax.get_yaxis().get_offset_text()
off.set_x(-.07)
off.set_fontsize(12)
#plt.setp(labels_x,fontsize=15, family='Times New Roman')
plt.setp(labels_y,fontsize=15)
#plt.xlabel(r"Burned Pixels", fontsize = 12)
plt.ylabel(r"Burned Pixels", fontsize = 15)
plt.tight_layout()
plt.show()

#help(off)

Variação intra-anual. Totais¶

months = 'Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec'.split(' ')

fig, ax = plt.subplots()

ax.plot( np.arange(12),monthly_total)
ax.set_xticks(np.arange(12))
ax.set_xticklabels(months, fontsize=15)


ax.set_title('Intra-annual Variability',fontsize=15)
labels_x = ax.get_xticklabels()
labels_y = ax.get_yticklabels()
off =ax.get_yaxis().get_offset_text()
off.set_x(-.07)
off.set_fontsize(12)

plt.setp(labels_y,fontsize=15)

plt.ylabel(r"Burned Pixels", fontsize = 15)
plt.tight_layout()
plt.show()

residuals_monthly_total = (np.array(monthly_total) - mean(monthly_total))/mean(monthly_total)

fig, ax = plt.subplots()

ax.plot( np.arange(12),residuals_monthly_total)
ax.set_xticks(np.arange(12))
ax.set_xticklabels(months, fontsize=15)


ax.set_title('Intra-annual Variability',fontsize=15)
labels_x = ax.get_xticklabels()
labels_y = ax.get_yticklabels()
off =ax.get_yaxis().get_offset_text()
off.set_x(-.07)
off.set_fontsize(12)

plt.setp(labels_y,fontsize=15)

plt.ylabel(r"$\frac{Burned Pixels - \langle Burned Pixels \rangle}{\langle Burned Pixels \rangle}$", fontsize = 15)
plt.tight_layout()
plt.show()

130070.096/3600

36.13058222222222

$\frac{Burned Pixels - \langle Burned Pixels \rangle}{\langle Burned Pixels \rangle}$

Variação intermensal, totais¶

ym = itertools.product(years,months)
ymlabels = [ m + ' ' + y[-2:] for y, m in ym]

fig, ax = plt.subplots(figsize=(16,12))
labels = ymlabels[::6]
ax.plot( np.arange(12*17),all_months)
ax.axhline(y=mean(all_months), color='r', linestyle='--', label='average')
ax.set_xticks(np.arange(0,12*17,6))
ax.set_xticklabels(labels,fontsize=12, rotation=30)
labels_y = ax.get_yticklabels()
plt.setp(labels_y,fontsize=15)
ax.set_title('Intra-monthly Variability',fontsize=15)
plt.legend()
plt.show()

Variação inter_anual em cada bioma¶

fig, ax = plt.subplots(5,3, figsize =(18,12) , sharey=True)
labels = years[::2]
for i in range(5):
    for j in range(3):
        slope, intercept, r_value, p_value, std_err = stats.linregress(years_float, anbio[i*3 + j])
        ax[i][j].plot(years, intercept + slope*years_float, 'r', label=r"$R^2$: {:5.3f} ".format(r_value**2))
        ax[i][j].plot(years, anbio[i*3 + j], 'o')
        ax[i][j].set_xticks(labels)
        ax[i][j].set_xticklabels(labels,fontsize=15)
        labels_y = ax[i][j].get_yticklabels()
        plt.setp(labels_y,fontsize=15)
        ax[i][j].set_title(biome_names_dict[i*3 + j],fontsize=15)
        ax[i][j].legend(fontsize=15)         
fig.tight_layout()
plt.show()

%%HTML
<h1 id="varintrabio"> Variação intra_anual em cada bioma</h1>

«««

len(monthly_biome[0])

15

ar_monthly_biome = np.array(monthly_biome)

ar_monthly_biome.shape

(12, 15)

monbio = ar_monthly_biome.T

monbio.shape

(15, 12)

fig, ax = plt.subplots(5,3, figsize =(18,12) , sharey=True)
labels = months
for i in range(5):
    for j in range(3):
        #slope, intercept, r_value, p_value, std_err = stats.linregress(np.arange(12), monbio[i*3 + j])
        #ax[i][j].plot(months, intercept + slope*years_float, 'r', label=r"$R^2$: {:5.3f} ".format(r_value**2))
        ax[i][j].plot(months, monbio[i*3 + j], 'o')
        ax[i][j].set_xticks(np.arange(12)) 
        ax[i][j].set_xticklabels(labels,fontsize=15)
        labels_y = ax[i][j].get_yticklabels()
        plt.setp(labels_y,fontsize=15)
        ax[i][j].set_title(biome_names_dict[i*3 + j],fontsize=15)
        ax[i][j].legend(fontsize=15)         
fig.tight_layout()
plt.show()

fig, ax = plt.subplots(5,3, figsize =(18,12) )
labels = months
for i in range(5):
    for j in range(3):
        #slope, intercept, r_value, p_value, std_err = stats.linregress(np.arange(12), monbio[i*3 + j])
        #ax[i][j].plot(months, intercept + slope*years_float, 'r', label=r"$R^2$: {:5.3f} ".format(r_value**2))
        ax[i][j].plot(months, monbio[i*3 + j], 'o')
        ax[i][j].set_xticks(np.arange(12)) 
        ax[i][j].set_xticklabels(labels,fontsize=15)
        labels_y = ax[i][j].get_yticklabels()
        plt.setp(labels_y,fontsize=15)
        ax[i][j].set_title(biome_names_dict[i*3 + j],fontsize=15)
        ax[i][j].legend(fontsize=15)         
fig.tight_layout()
plt.show()

type(df_finall)

pandas.core.frame.DataFrame

biome_grouped.sum()/areas_biome

 biome_grouped.sum()[14][16*12]/areas_biome[14]

29.061933349349864

biomes_pa = biome_grouped.sum()/areas_biome

biomes_pa.index.get_level_values(level=1)

Index(['Jan', 'Fev', 'Mar', 'Abr', 'Mai', 'Jun', 'Jul', 'Ago', 'Set', 'Out',
       ...
       'Mar', 'Abr', 'Mai', 'Jun', 'Jul', 'Ago', 'Set', 'Out', 'Nov', 'Dez'],
      dtype='object', length=204)

biomes_pa.index.get_level_values(level=1)

Index(['Jan', 'Fev', 'Mar', 'Abr', 'Mai', 'Jun', 'Jul', 'Ago', 'Set', 'Out',
       ...
       'Mar', 'Abr', 'Mai', 'Jun', 'Jul', 'Ago', 'Set', 'Out', 'Nov', 'Dez'],
      dtype='object', length=204)

intra_biome = biomes_pa.groupby(biomes_pa.index.get_level_values(level=1), sort=False).sum()

for a, b in biomes_pa.groupby(biomes_pa.index.get_level_values(level=1),sort=False):
    print(a)
    print(b)

Jan
BIOME_NUM      0.0        1.0         2.0        3.0        4.0       5.0   \
2001 Jan   0.005549  27.050378  294.159587   2.457691   0.708703  0.235960   
2002 Jan   0.000000  38.795751  314.488409   2.457691  10.087252  0.020225   
2003 Jan   0.002929  69.941757  263.764176   1.942904  27.791677  1.289913   
2004 Jan   0.000000  46.164329  356.735676   5.131260   1.690783  0.038203   
2005 Jan   0.000000  81.842110  281.295875   7.339861   1.352349  0.379783   
2006 Jan   0.004316  33.638544  229.140982   9.664704   6.664161  2.114649   
2007 Jan   0.001387  59.109110  429.797157   2.457691   8.703067  1.114629   
2008 Jan   0.000308  51.483254  265.298773  13.816209   0.824975  0.159554   
2009 Jan   0.007861  38.517033  158.662637   6.293682   2.587043  1.373061   
2010 Jan   0.000000  38.863134  300.401911   2.424479   1.015992  1.116876   
2011 Jan   0.000154  33.209746  333.621958   4.931988   0.574437  0.858444   
2012 Jan   0.000000  37.056059  265.622811   2.025934   2.193934  0.707879   
2013 Jan   0.000462  49.828096  235.612579   4.965200   6.802580  0.979795   
2014 Jan   0.000000  33.885409  170.587860   1.262058   8.274661  0.955075   
2015 Jan   0.000000  55.159273  272.210573   2.291631   2.052055  1.155079   
2016 Jan   0.000000  83.399258  207.277563   3.786173   4.299972  0.444953   
2017 Jan   0.000462  28.227121   85.338256  21.471584  13.039720  0.961817   

BIOME_NUM      6.0          7.0         8.0          9.0        10.0  11.0  \
2001 Jan   0.054303   816.822779  136.908757  1317.788732   8.662509   0.0   
2002 Jan   0.000000   772.452931   17.127363  1716.037356   5.985700   0.0   
2003 Jan   0.000000   998.604149   24.766503   735.198363  69.471880   0.0   
2004 Jan   0.000000   667.036472    6.575252  1290.950938   9.954249   0.0   
2005 Jan   0.000000  1054.433495    7.387145  1131.155263   9.205507   0.0   
2006 Jan   0.094337   703.938134    9.546631  1314.815173  14.864560   0.0   
2007 Jan   0.000000   877.533672    2.635942  1228.657709  12.928010   0.0   
2008 Jan   0.000000   840.320232    3.999389  1122.035716   5.707838   0.0   
2009 Jan   0.000000   622.330905    9.943817  1177.311724   7.705900   0.0   
2010 Jan   0.000000   770.405220    2.907129   932.210880   7.616814   0.0   
2011 Jan   0.000000   722.594126    4.052792  1213.089136   9.807895   0.0   
2012 Jan   0.000000   820.254560    7.760967  1352.808550   8.598876   0.0   
2013 Jan   0.000000   849.253217    8.885769  1189.944617   6.095997   0.0   
2014 Jan   0.007927   694.905492   29.568607  1221.185930   4.950611   0.0   
2015 Jan   0.000000   674.351554   13.415016  1140.691487   6.643237   0.0   
2016 Jan   0.000000   992.973780    3.777432  1048.236517   5.807529   0.0   
2017 Jan   0.000000   504.634307   48.590944   988.850558   5.652690   0.0   

BIOME_NUM       12.0        13.0        14.0  
2001 Jan   87.917982  150.205357    6.904017  
2002 Jan   21.373149   71.646836   30.182481  
2003 Jan   37.652683   16.461863   54.364612  
2004 Jan    4.295271    9.402703   19.989116  
2005 Jan   42.151334   11.301005  100.668513  
2006 Jan   20.310714    1.830793   15.109314  
2007 Jan    8.769639    5.669590   39.580618  
2008 Jan   21.600813   30.440342   46.340048  
2009 Jan   21.464215    4.134955   34.773109  
2010 Jan   23.294638    5.230377   25.555705  
2011 Jan    5.706792   12.747235   29.170373  
2012 Jan   16.276498   40.269399   40.195112  
2013 Jan    7.804341   30.202451   16.808208  
2014 Jan   58.476401    8.624335   14.711700  
2015 Jan   11.122173   11.515187   69.112458  
2016 Jan   30.476696    9.644612   38.532364  
2017 Jan   27.574732    2.441994   29.061933  
Fev
BIOME_NUM      0.0         1.0         2.0        3.0        4.0       5.0   \
2001 Fev   0.000000   59.446635  130.801451   8.435858   0.742616  0.208993   
2002 Fev   0.003391   42.650640  112.967112   3.819385   3.579504  2.546118   
2003 Fev   0.000000   50.174197  229.266318   8.286404   8.082951  1.269688   
2004 Fev   0.000000  113.045085  178.939490   8.136950   3.245915  0.921367   
2005 Fev   0.001387  107.077450  143.643764  16.340324   2.214697  1.806778   
2006 Fev   0.000000   52.210985  265.097013  21.886734   3.701312  1.543851   
2007 Fev   0.002158  110.519468  119.723007   5.197684   5.190696  2.851742   
2008 Fev   0.002312   74.455769  164.935530   6.559378  17.835231  3.591083   
2009 Fev   0.000000  120.912905  306.726773  42.710683  12.481893  2.791067   
2010 Fev   0.000000   94.073863   79.355774   7.638769   2.551746  2.905676   
2011 Fev   0.000771   49.249832  149.883031  14.380813   4.991373  0.723610   
2012 Fev   0.002004  104.831164  157.024103  18.067350   6.722297  1.182046   
2013 Fev   0.002774   71.312684  160.668006  15.410387   4.876486  2.449487   
2014 Fev   0.000154   68.994115  188.003394   4.201323  15.160292  2.487690   
2015 Fev   0.000000   61.403788  179.355238   8.469070  10.694910  1.022492   
2016 Fev   0.000000   94.105716  147.737041  17.868078   5.158859  1.487670   
2017 Fev   0.002620   76.634060  324.906548  24.942242   5.839187  3.301189   

BIOME_NUM      6.0         7.0        8.0          9.0        10.0  11.0  \
2001 Fev   0.000000  317.193617  20.659474   510.126430   8.036789   0.0   
2002 Fev   0.001189  357.576610  21.891082  1204.121108   5.519062   0.0   
2003 Fev   0.008324  377.099455  21.467196   400.701337  18.175573   0.0   
2004 Fev   0.000000  479.696553   4.898896   776.989184   7.016547   0.0   
2005 Fev   0.046772  365.888823  11.448281   536.149810   5.930552   0.0   
2006 Fev   0.000396  266.026460   7.617446   618.604530   7.642267   0.0   
2007 Fev   0.000000  470.997401   7.161851  1031.948230  13.456160   0.0   
2008 Fev   0.042808  379.819157   9.573333   987.373248   7.472581   0.0   
2009 Fev   0.000000  257.423068   6.117154   777.737309  10.439978   0.0   
2010 Fev   0.077689  308.803459   3.132423   380.615606   6.129934   0.0   
2011 Fev   0.000000  394.403668  10.406921  1092.981957   5.190294   0.0   
2012 Fev   0.001189  390.678482   4.648569   893.109520   5.597542   0.0   
2013 Fev   0.000000  343.137410   5.544740   604.304929   5.018486   0.0   
2014 Fev   0.005946  360.093100   6.239814   714.430797   2.948307   0.0   
2015 Fev   0.017837  308.463287   3.942648   920.098834   6.204172   0.0   
2016 Fev   0.000000  290.776033   9.225379   373.768844   2.536817   0.0   
2017 Fev   0.072140  273.661670  18.064419   526.282139   2.203807   0.0   

BIOME_NUM       12.0       13.0        14.0  
2001 Fev   11.783918   6.511449   62.714495  
2002 Fev   15.751352  21.735638   72.835567  
2003 Fev   46.558920   4.743344   66.039990  
2004 Fev    8.642147   3.420480  124.163857  
2005 Fev   47.172097  11.537288  153.514964  
2006 Fev   11.908374   4.948685   96.692378  
2007 Fev    9.695475  22.647016  134.718689  
2008 Fev    8.733213   6.416213   98.246685  
2009 Fev   10.047596   3.844021  104.897675  
2010 Fev   69.862665   7.766402  128.248432  
2011 Fev    6.274435  14.488818  109.018397  
2012 Fev   13.034555  36.203963   89.173868  
2013 Fev    8.241457   7.104568   80.173343  
2014 Fev    5.907136   7.041881  129.079806  
2015 Fev   46.901935   6.745321   98.318978  
2016 Fev   12.211927   4.665387  102.403554  
2017 Fev   14.904440   5.218321   74.389874  
Mar
BIOME_NUM      0.0         1.0         2.0         3.0        4.0        5.0   \
2001 Mar   0.000000   44.769512   57.736912   34.308037   4.959537   1.622504   
2002 Mar   0.000617   43.605021   71.166163   42.528017  34.784582  13.964323   
2003 Mar   0.000308  141.255666  131.024609  156.827253  45.210960  25.045442   
2004 Mar   0.000308  220.293442  180.354866   84.740520   8.633857   3.462990   
2005 Mar   0.000925  104.012774  116.048534   96.680248  19.184811   7.764200   
2006 Mar   0.000154  128.668012   70.255187   87.596756  13.741502   5.892252   
2007 Mar   0.000771  205.113401  105.202419   32.547799  30.962846   6.795641   
2008 Mar   0.000771  103.620118  127.454073   55.862651  80.434329  68.792381   
2009 Mar   0.001541  125.883892  141.947148   52.358781  26.440020   9.977727   
2010 Mar   0.000000  227.919910  146.734356   73.249154  16.511949   9.132766   
2011 Mar   0.000000   98.516816  141.485546  114.083357  25.596359  14.618269   
2012 Mar   0.016492  159.148157  127.753656   47.260733  33.266823  15.013782   
2013 Mar   0.000308   88.807005  102.169909  104.152958   6.107719   4.986616   
2014 Mar   0.001233  140.983074   85.124268   18.017532  29.973154  19.959948   
2015 Mar   0.001387  156.261738   85.625611   44.736618  24.295224  11.406969   
2016 Mar   0.000000   86.784919   87.951962   32.514587  17.540399   8.584441   
2017 Mar   0.000154   94.194538  140.553171   79.526230  45.869832  10.694595   

BIOME_NUM       6.0         7.0        8.0         9.0       10.0      11.0  \
2001 Mar    0.003567  145.483777  15.521933  320.159545  3.064967  0.000000   
2002 Mar    1.120946  153.289352  17.100661  591.558397  2.814679  0.000000   
2003 Mar   31.788821  174.533097  32.840379  586.880249  4.503062  0.318556   
2004 Mar    0.242977  217.509412  16.932108  441.422056  2.536817  0.000000   
2005 Mar   11.667674  178.737084  43.949049  510.079081  1.921703  0.004013   
2006 Mar    0.395582  167.592705  26.106586  453.363643  2.328951  0.000000   
2007 Mar    2.539570  207.076554  41.380696  526.736695  8.301925  0.005518   
2008 Mar    7.724938  167.692920  22.906575  744.639888  4.976064  0.011538   
2009 Mar    4.075124  145.991529  20.865577  503.819454  4.017334  0.000000   
2010 Mar    0.209286  168.825340  11.843797  271.938638  2.131690  0.000000   
2011 Mar   20.190513  153.826054  26.959367  573.366653  6.250836  0.015050   
2012 Mar    2.638267  149.672731  19.966069  683.984957  7.778017  0.000502   
2013 Mar    0.057871  143.947159   9.579174  396.828134  2.477427  0.000000   
2014 Mar   41.697782  135.308692  90.566583  447.871081  2.015030  0.000000   
2015 Mar   12.897623  146.950802  51.473874  477.701375  6.706869  0.028093   
2016 Mar    1.627513  102.840953  29.753014  152.816333  1.628993  0.000502   
2017 Mar    3.043362  111.447128  58.855179  548.839522  2.218654  0.049163   

BIOME_NUM       12.0       13.0        14.0  
2001 Mar   13.559701   9.062745   85.992960  
2002 Mar   15.317272  14.328885   77.064729  
2003 Mar   28.200050   8.927324  230.182079  
2004 Mar   17.250903   5.462641  185.432485  
2005 Mar   21.394398   9.696048  160.310540  
2006 Mar   18.413510   8.031216  242.327365  
2007 Mar    7.248840  25.590911  108.114730  
2008 Mar    8.074503   9.986981   99.981726  
2009 Mar   25.000604   8.004694  140.393718  
2010 Mar   24.481529   5.185772  152.864324  
2011 Mar   31.800186   7.309106  111.910131  
2012 Mar   22.074356   9.286571  153.153497  
2013 Mar    7.136525   9.044662  171.660599  
2014 Mar   24.262971   9.961263  199.493545  
2015 Mar   16.133829  15.651347  136.887490  
2016 Mar   11.213238   6.606284  151.816070  
2017 Mar   56.041908   7.587582  131.031727  
Abr
BIOME_NUM      0.0        1.0         2.0         3.0         4.0        5.0   \
2001 Abr   0.000000  23.675130   52.096810  137.597482   21.573224   6.919239   
2002 Abr   0.000000  32.785237   50.562214   98.440487   14.291024  10.946285   
2003 Abr   0.002620  68.361945   64.064831  141.184382   39.548257   7.528240   
2004 Abr   0.000000  42.651253   70.943004  103.173202   39.675602   2.991071   
2005 Abr   0.000617  59.959354   96.459497  232.783189   31.331040   4.752904   
2006 Abr   0.000000  43.317114   51.671892  156.246042   68.094315  13.328355   
2007 Abr   0.000308  75.180436   74.580793   82.199799   16.161058  16.290212   
2008 Abr   0.000617  44.047907   91.161776  204.818651  103.763392  89.066942   
2009 Abr   0.001233  82.919617   91.901561  281.986826  122.585547  12.108106   
2010 Abr   0.000000  59.825814   74.849806   78.529869   38.862393  17.119442   
2011 Abr   0.000000  36.284836  116.941168  521.844179   53.620579  22.697081   
2012 Abr   0.000462  37.726821   94.356305  172.719214   47.346066  17.256523   
2013 Abr   0.000154  83.766798   89.376508  170.859340   40.559404  63.565310   
2014 Abr   0.000000  83.349640   89.116666   85.753487   72.750023   9.141755   
2015 Abr   0.000000  62.071487   77.032480   66.739594   32.100647  12.296874   
2016 Abr   0.000000  93.673856  107.751929  267.655831   24.626737  10.206945   
2017 Abr   0.000308  60.064715  157.430679  259.585305   56.259529   0.991031   

BIOME_NUM       6.0         7.0         8.0         9.0       10.0      11.0  \
2001 Abr    2.560974  132.973701   42.203437  289.126569  0.702079  0.000000   
2002 Abr    3.412783  144.868016   23.242847  426.961466  2.555907  0.000000   
2003 Abr   12.111613  118.742724   74.783476  536.263449  3.637660  0.024080   
2004 Abr    7.481961  107.863913   60.563242  291.702392  1.187807  0.022073   
2005 Abr    5.500090  190.885832   67.004152  349.487932  1.187807  0.000502   
2006 Abr   20.666955  106.652991   42.204271  387.235091  0.863281  0.000000   
2007 Abr    7.538246  113.796038   34.375717  388.608231  2.163506  0.000000   
2008 Abr   23.620736  131.826805  182.177863  547.002355  4.766077  0.016555   
2009 Abr   15.439175  186.535978   76.559127  592.798958  1.756258  0.004013   
2010 Abr   12.617386  104.058556   65.004040  171.860371  1.989577  0.005017   
2011 Abr   19.048955   91.315203  106.399756  409.356858  1.639598  0.054180   
2012 Abr    6.084742  172.914638  132.970281  305.414857  4.978185  0.005518   
2013 Abr    8.367461  145.995426   16.242040  462.985097  1.580208  0.095316   
2014 Abr    8.024201   88.906722   95.020732  628.699480  4.923037  0.011538   
2015 Abr    4.339506  177.553443  108.767013  421.563603  5.130903  0.008027   
2016 Abr    3.311311  130.202221   56.154986  243.812933  1.022363  0.006522   
2017 Abr    9.359982   97.633710   67.168533  379.753842  1.677778  0.003010   

BIOME_NUM       12.0       13.0        14.0  
2001 Abr   20.769079  42.457831   64.666416  
2002 Abr   18.349764  46.175674   43.448313  
2003 Abr    9.224968  12.533053  172.347386  
2004 Abr   60.792509   8.747299  134.646395  
2005 Abr   25.131132  12.570022   87.511120  
2006 Abr   23.649795   8.800744   79.125090  
2007 Abr    9.255323   5.773667   87.547267  
2008 Abr   23.668008  11.710482   77.968396  
2009 Abr   38.812254  10.966672  198.842905  
2010 Abr   32.486215   6.714379   74.426021  
2011 Abr   24.876148  30.975997   82.992785  
2012 Abr   27.814538  32.742897   87.294240  
2013 Abr   20.857109  10.393244  117.187547  
2014 Abr   51.069714   9.424804  149.683415  
2015 Abr   30.343133  27.108267  232.423173  
2016 Abr    9.100511  16.272997  144.478293  
2017 Abr   46.762301  11.181658  108.620783  
Mai
BIOME_NUM      0.0        1.0         2.0         3.0        4.0        5.0   \
2001 Mai   0.000000   9.948100   58.293280  194.406676   1.519144   2.179819   
2002 Mai   0.000154  21.687960   48.083014  259.369427  17.277404  18.759924   
2003 Mai   0.000617  53.719126  100.271534  371.842000  42.043943  91.251255   
2004 Mai   0.000771  25.977772   47.422710   90.004628  42.529100   4.678745   
2005 Mai   0.000308  32.214936   89.235887  434.197605  27.980618   3.242761   
2006 Mai   0.000771  18.705368   52.842710  259.469063  63.459370   6.301249   
2007 Mai   0.000154  27.569223   63.820274  304.786891  41.387840   6.020345   
2008 Mai   0.000617  22.302366   78.536507  332.236642  29.508067  17.622823   
2009 Mai   0.000308  20.860381   63.083545  189.673961  52.255080  11.227190   
2010 Mai   0.000000  23.389673   54.517927  117.969166   5.717378   2.804550   
2011 Mai   0.000000  24.325677   53.622236  578.952282  23.468174  22.355501   
2012 Mai   0.000308  17.802443   56.975728  396.020365  31.780900  72.657626   
2013 Mai   0.000154  28.638154   80.835345  255.749314  33.235679   4.739420   
2014 Mai   0.002466  21.675709   37.276640   77.998476  22.227943   6.566423   
2015 Mai   0.000000  23.839911   38.441344   85.022822   4.246681   4.089969   
2016 Mai   0.000000  34.980680   67.895209  157.275616  20.716413  20.085793   
2017 Mai   0.000000  16.789256   54.548497  202.360960   6.187309   2.905676   

BIOME_NUM       6.0         7.0         8.0         9.0        10.0      11.0  \
2001 Mai    1.100731  360.944364   11.273052   95.712628   6.766259  0.000000   
2002 Mai    7.086776  436.149574   22.342505  332.981836  26.929288  0.170064   
2003 Mai   65.871461  489.978533  116.057365  307.128915  22.721057  1.783413   
2004 Mai   24.138401  404.860466   75.526112  175.809334  20.389985  0.070735   
2005 Mai    3.569747  412.360944   18.318084  403.068821  20.928740  0.254845   
2006 Mai    6.422056  257.083453   96.340789  271.645070  28.734331  0.021572   
2007 Mai    6.053032  373.176624   42.794208  284.902979  25.073339  0.055685   
2008 Mai   20.122733  338.210767   56.065703  134.009043   4.121267  0.165047   
2009 Mai    6.109713  232.606685   34.672771  359.885920   5.489367  0.137957   
2010 Mai    2.485267  265.965217   18.344785  101.707096   5.934794  0.107356   
2011 Mai    6.181853  313.276354   25.096935  229.522802  12.015944  0.134446   
2012 Mai    7.674599  409.334474   38.753933  204.408536  22.899228  0.078259   
2013 Mai    1.395237  340.854752   29.480992  400.909676  19.499129  0.312536   
2014 Mai    7.249686  273.713447   30.131008  122.389433   5.296348  0.119898   
2015 Mai    8.132808  304.752020   24.959255  171.244825  15.628150  0.246317   
2016 Mai   15.270716  270.625179   35.902710  334.165578  14.576092  0.365211   
2017 Mai    2.308484  323.876235   23.780216  126.234227  10.959643  0.007023   

BIOME_NUM       12.0       13.0        14.0  
2001 Mai    2.653051  42.684068   25.989465  
2002 Mai    5.497340  34.714736   44.677301  
2003 Mai    6.914932  14.200697  126.585685  
2004 Mai   32.977970  23.333363   91.161935  
2005 Mai   10.499890  22.835078   45.364088  
2006 Mai   13.590056  17.035292  150.984696  
2007 Mai    5.375919  10.286756  120.838362  
2008 Mai    4.389373  14.379115   80.751690  
2009 Mai   23.400881  12.101876   76.124915  
2010 Mai   13.483813  11.582294   75.835742  
2011 Mai    5.855532  33.761566  135.911529  
2012 Mai   28.670557  20.209844   30.688534  
2013 Mai   13.386676  19.002710   72.944007  
2014 Mai    8.195924   8.763372   72.329513  
2015 Mai   13.444351  17.339888   53.894705  
2016 Mai    5.454843  17.217326   86.065253  
2017 Mai   29.593357  31.725031   39.508325  
Jun
BIOME_NUM      0.0        1.0        2.0         3.0        4.0        5.0   \
2001 Jun   0.012331  46.332785  43.506737    3.371022   7.151395   3.089949   
2002 Jun   0.005086  18.960196  23.932985   53.238900  17.788168  41.270484   
2003 Jun   0.001541  29.766503  26.632286   85.238701  27.395108  13.573304   
2004 Jun   0.003699  30.088101  29.817644   15.842143  23.683414   6.460803   
2005 Jun   0.002004  24.963360  29.435523  181.204890  47.078918  13.600271   
2006 Jun   0.003083  17.581919  27.378185   32.647435  84.112802   7.723749   
2007 Jun   0.004778  32.542660  38.098964   65.676809  48.413965   3.961877   
2008 Jun   0.002774  25.039318  32.599483   57.506647   4.985836  10.851901   
2009 Jun   0.001387  30.837884  37.481457   35.719549  22.629357   5.991131   
2010 Jun   0.000617  21.445996  27.992635   50.615149  78.823829   4.804590   
2011 Jun   0.000462  20.926538  28.069060  197.130064  44.138908  30.991628   
2012 Jun   0.011560  25.611456  26.543634  122.502610  97.373992  24.521836   
2013 Jun   0.000771  32.344801  28.136313   70.774857  29.009760  13.393525   
2014 Jun   0.002774  18.479943  28.582630  105.863378  25.660723   7.397900   
2015 Jun   0.001695  25.143455  25.177170    7.921072  16.087696   8.759725   
2016 Jun   0.002158  22.538817  27.796990   60.528943   3.630027  13.440717   
2017 Jun   0.026049  16.990791  24.217283  191.616865  12.124773  13.229477   

BIOME_NUM       6.0         7.0         8.0         9.0        10.0  \
2001 Jun    4.647092  726.834858   40.303456  405.066977  49.474295   
2002 Jun   19.215829  479.351371   30.773513  481.091611  22.337141   
2003 Jun   39.553794  516.877145   24.638837  435.361298  22.731662   
2004 Jun    9.640219  686.265912  114.286720  512.598083  21.582034   
2005 Jun   15.339289  666.317713   47.410235  678.767023  34.444291   
2006 Jun   17.903037  565.690938   96.106316  459.708499  25.525130   
2007 Jun   11.965747  562.908323   68.961708  690.481332  18.854320   
2008 Jun   21.725671  560.376243   61.024678  632.212826  28.662214   
2009 Jun    7.481168  553.797736   22.770564  732.594131  29.531859   
2010 Jun   17.815042  571.040707   99.994726  574.616684  34.297936   
2011 Jun   21.578616  592.646895   57.168810  530.325801  34.722153   
2012 Jun   40.369928  737.452555   33.136599  831.261378  24.464588   
2013 Jun   24.323508  633.827704   13.668680  761.202803  14.569729   
2014 Jun    8.870857  589.431131   31.023840  488.127773  24.297023   
2015 Jun   35.863153  515.917315   41.262208  545.790204  20.739964   
2016 Jun   13.304699  585.744361   13.601092  758.920549  16.287808   
2017 Jun   10.505900  572.259980   34.535091  345.463210  20.595730   

BIOME_NUM       11.0       12.0        13.0       14.0  
2001 Jun    0.206685  14.910511   37.437618   2.385681  
2002 Jun    0.892459  17.296436   15.433147   6.940163  
2003 Jun    4.474334  22.478081    7.319955  21.037370  
2004 Jun   17.104708  24.208332  101.585679  36.472003  
2005 Jun    2.939746  20.219648   30.920141  24.832771  
2006 Jun    0.163041  14.391436   70.654286  30.363214  
2007 Jun    1.069546  19.096503   22.022955  57.328640  
2008 Jun    0.657179  23.625510   15.253926  20.531316  
2009 Jun    0.677747  22.365766   14.906332  27.037719  
2010 Jun    1.528066  19.655040   41.327449  37.881724  
2011 Jun    0.349158  13.077052   23.037607  33.218802  
2012 Jun    1.951971  21.169769   27.853282  17.314261  
2013 Jun    0.722897  16.091331    8.076222  19.374622  
2014 Jun    0.199160  18.316373   12.800278  22.447090  
2015 Jun    2.782224  34.462343   18.921538  21.615717  
2016 Jun    2.212335  24.126372    3.559517  25.953319  
2017 Jun    1.250646  38.171758   36.515390  17.928755  
Jul
BIOME_NUM      0.0        1.0        2.0        3.0        4.0        5.0   \
2001 Jul   0.025586  27.403217  23.248225   0.016606  25.104973   4.184353   
2002 Jul   0.011868  40.711863  40.098220  13.185180  24.835749  36.196226   
2003 Jul   0.008323  56.345204  45.426511   4.965200   6.426773  16.953146   
2004 Jul   0.013872  46.576587  43.531192   0.730665  10.345403  11.406969   
2005 Jul   0.007090  69.533787  40.932771   6.111015  14.345007  10.678864   
2006 Jul   0.008786  35.078691  49.522845   0.016606  14.244653  19.993656   
2007 Jul   0.061962  54.681470  45.594644   1.046179  20.581455  30.643307   
2008 Jul   0.005703  40.465611  53.279856   0.215878  22.499244  23.285857   
2009 Jul   0.004932  53.434895  28.897497   2.324843  12.885383   3.687714   
2010 Jul   0.002774  48.006320  49.672637   0.232484  26.135499   6.069784   
2011 Jul   0.002929  47.389464  30.887582   4.732716   5.042588   4.979874   
2012 Jul   0.007553  51.647422  47.019190   0.481575   9.053958  11.597984   
2013 Jul   0.004624  46.532482  29.151225   0.232484   4.751909   9.779970   
2014 Jul   0.001850  37.260657  27.289533   0.265696   6.905702  32.270305   
2015 Jul   0.009865  49.177549  28.506206   3.121932  11.771806  29.641040   
2016 Jul   0.006628  50.751236  51.726918   1.295270   8.793731   6.872047   
2017 Jul   0.015876  44.176546  27.524920   0.813695   6.546505  57.333726   

BIOME_NUM       6.0         7.0         8.0         9.0        10.0  \
2001 Jul   32.171323  530.560284  161.060291  787.093604  26.263268   
2002 Jul   57.357341  814.174340  111.809319  883.222909  69.032815   
2003 Jul   34.327202  846.399895   31.670518  741.884137  24.683060   
2004 Jul   28.758144  818.818713   60.663373  781.222245  29.005830   
2005 Jul   26.066366  962.167370  141.452189  676.816217  32.274421   
2006 Jul   36.565528  776.525525  112.990862  807.861170  32.369870   
2007 Jul   18.372345  971.532946   82.919932  751.742339  57.710466   
2008 Jul   39.238676  781.773410  124.001905  780.474120  31.118430   
2009 Jul   22.155341  797.223660  100.269251  574.872372  22.568339   
2010 Jul   26.419139  812.357679   86.846726  810.863139  33.508892   
2011 Jul   34.131393  932.011239   32.047677  873.809794  25.728755   
2012 Jul   85.249011  837.244212   82.343346  663.009053  20.739964   
2013 Jul   64.322827  765.446286   13.370791  555.459007  25.370291   
2014 Jul   68.978639  789.900227   78.223801  810.086605  30.893595   
2015 Jul   51.616652  877.116669   55.152845  834.954653  26.581431   
2016 Jul   39.127295  784.563820   49.426201  614.863906  18.928558   
2017 Jul   25.785733  718.447484  101.440781  719.885479  26.687485   

BIOME_NUM       11.0       12.0        13.0       14.0  
2001 Jul    3.521676  20.975495   42.202259   2.349534  
2002 Jul    1.341447  33.026539   72.953225  16.193714  
2003 Jul   16.025631  35.318362   13.975666  26.097905  
2004 Jul   11.108830  35.139266   22.263659  22.627824  
2005 Jul    3.118338  30.759000  100.478604  28.339000  
2006 Jul    2.398953  20.250004   36.399258  17.061235  
2007 Jul    1.543618  50.526355   39.236263  21.868743  
2008 Jul    0.894967  21.312438   14.471941  15.868394  
2009 Jul    3.174525  38.542092   23.747260  22.591677  
2010 Jul   18.182281  19.472909   46.229521  14.386380  
2011 Jul    1.311849  11.377157   40.657578  22.121770  
2012 Jul    1.666525  36.119741   30.464854  16.880501  
2013 Jul    1.603817  14.628207    8.517043  39.291445  
2014 Jul    0.648149  13.708442   19.519881  33.038069  
2015 Jul    3.372682  29.053033   22.235128  19.699942  
2016 Jul    7.814909  19.582188    9.605633  14.422527  
2017 Jul    5.416959  48.623079  102.125755  27.543773  
Ago
BIOME_NUM      0.0         1.0         2.0       3.0        4.0        5.0   \
2001 Ago   0.021425   82.596794   94.099520  0.000000  50.715174  17.067755   
2002 Ago   0.008632  116.581438  106.217332  1.129209  54.015764  34.587205   
2003 Ago   0.005857   99.114070   83.250348  0.116242  10.890772  41.454758   
2004 Ago   0.000617  101.266633  117.521991  0.298908  37.110014   8.004654   
2005 Ago   0.011868  154.063845  128.780796  0.398544  33.324959  12.249682   
2006 Ago   0.033293   98.499664  122.617953  0.381938  24.237089  24.634198   
2007 Ago   0.025586  123.725203  125.656577  0.000000  33.171314  54.048267   
2008 Ago   0.008323   83.138304   58.797679  2.922660  74.969565  10.694595   
2009 Ago   0.004316   62.814532   42.855603  1.627390  39.596703  20.627377   
2010 Ago   0.012177  157.809696  248.363183  0.000000  36.853940  23.919577   
2011 Ago   0.009402   71.233663   66.758018  0.083030  18.042858  11.042916   
2012 Ago   0.031752   83.709217  113.544879  0.000000   9.152235  47.502069   
2013 Ago   0.005241   69.721233   53.539698  2.773205  17.278788  36.384994   
2014 Ago   0.006782   67.180302   53.408249  0.963149  11.391155  36.032178   
2015 Ago   0.003545   75.837109   81.257207  0.714059  17.249720  81.147683   
2016 Ago   0.010173   78.819089  110.866977  3.653324  19.042240  16.083466   
2017 Ago   0.015568   64.544423   54.050211  0.631029  11.328866  88.520863   

BIOME_NUM       6.0          7.0         8.0          9.0        10.0  \
2001 Ago   17.046473  1240.009087  187.251154  1231.583918  54.378242   
2002 Ago   46.529981  1027.067217  222.770864   974.996044  52.233826   
2003 Ago   28.845742  1136.837427   49.532172   723.076847  63.884943   
2004 Ago   15.795912  1029.077625   82.954978   888.279854  39.469140   
2005 Ago   20.119166  1069.821389   86.905970  1124.602069  44.443083   
2006 Ago    5.837008  1111.337917  154.950647  1027.279552  50.908148   
2007 Ago    5.538934  1165.708589  129.941327   900.979036  33.860992   
2008 Ago    4.886106   904.596523  218.920003   667.081124  53.924330   
2009 Ago   11.313711   994.818835   52.012911   685.253929  51.317518   
2010 Ago   21.872726  1256.384091  117.253928  1065.945295  55.977540   
2011 Ago    8.148266  1067.495306   50.418329   519.511135  67.573509   
2012 Ago   30.127617  1088.513013   66.778023   750.653296  44.638223   
2013 Ago   40.857072  1027.416853   23.762693   415.701714  40.113949   
2014 Ago   33.040175   977.326442  114.043069   537.646060  52.363212   
2015 Ago   14.210811  1004.760086  138.258019   645.641193  31.228727   
2016 Ago    5.492955   961.328354   62.429846   467.663244  28.235876   
2017 Ago   40.606564   884.540316  113.463145   663.113222  36.071162   

BIOME_NUM       11.0       12.0        13.0       14.0  
2001 Ago    3.269339  35.227296  119.378443  10.590978  
2002 Ago    8.248848  23.753002  190.515342  51.545170  
2003 Ago   13.101436  85.550271   24.673908  16.446741  
2004 Ago   12.954449  24.323682   17.791156  33.074215  
2005 Ago    5.107433  38.080692   19.368788  27.724506  
2006 Ago    1.594286  16.674152   64.809895  29.531840  
2007 Ago    4.182366  90.176415   48.029773  13.012806  
2008 Ago    1.853144  27.347067   69.790728  16.374448  
2009 Ago    2.638748  41.774929   16.531382  41.460246  
2010 Ago    8.679777  21.773839   67.479332   7.410070  
2011 Ago    1.060516  20.377496  153.561140  37.773284  
2012 Ago    2.208823  57.608240   61.137460  34.917696  
2013 Ago    2.466176  26.815850   10.589343  36.652737  
2014 Ago    0.844299  27.189220   33.595606  32.025961  
2015 Ago    4.995562  25.128096   44.712165  43.845927  
2016 Ago    8.049688  32.039992   16.054797  31.989815  
2017 Ago    0.864867  44.813492   75.250154   8.530617  
Set
BIOME_NUM      0.0         1.0         2.0       3.0        4.0        5.0   \
2001 Set   0.002774   60.699336   81.425340  0.531393  34.585260  10.092336   
2002 Set   0.002158  134.690165   92.635233  0.033212  46.525244  56.965179   
2003 Set   0.002774  112.037411   99.085431  0.531393  23.748471  12.501372   
2004 Set   0.002158  141.682626  278.455954  0.049818  34.511206   2.800056   
2005 Set   0.005549  163.125559  143.059883  0.000000  27.800674   6.793394   
2006 Set   0.005549  136.191569  105.954433  0.116242  26.828976  16.283470   
2007 Set   0.001541  211.957620  303.079813  0.016606  21.596063  16.231784   
2008 Set   0.006628  101.886551  135.065918  0.431757  41.672290   5.775396   
2009 Set   0.002620  100.949323   78.496767  0.614423  35.068340   8.676577   
2010 Set   0.002158  185.279683  236.126149  0.033212  29.164789   7.382169   
2011 Set   0.012793  112.613225  142.717503  0.763877  37.641541  16.429541   
2012 Set   0.012639  102.774162  170.019264  0.033212  20.890128  22.721801   
2013 Set   0.004778   77.650309   73.419147  1.693814  19.818076   5.258532   
2014 Set   0.003083   85.466674   82.036733  0.016606  33.822573  14.780070   
2015 Set   0.010481  117.677322  155.248006  0.016606  43.558242  19.568929   
2016 Set   0.000771   83.842144   86.081099  0.415151  24.799760   8.445112   
2017 Set   0.038688  135.997998  130.547723  0.033212  14.984500  44.758194   

BIOME_NUM       6.0          7.0         8.0         9.0        10.0  \
2001 Set    2.031418  1041.333825   59.118856  602.505641  41.957172   
2002 Set   11.333530   846.622593  330.111849  554.654062  54.346426   
2003 Set   13.546884   804.655102  101.769544  393.835635  32.446229   
2004 Set    1.708770  1207.745116   65.342816  794.840010  37.352297   
2005 Set    3.478581   930.333207  108.318094  658.491894  56.789915   
2006 Set    6.130325  1066.804384  129.574181  488.118303  35.326662   
2007 Set    5.014927  1253.876509  113.957958  709.506431  26.099944   
2008 Set    1.744840  1183.549505   79.214261  633.197699  46.761428   
2009 Set    4.239223   990.205640   31.184884  551.813082  38.663128   
2010 Set    1.388103  1241.331358   96.044569  620.422757  44.856694   
2011 Set    5.684800  1325.134391   54.897511  443.855829  39.252789   
2012 Set    4.175803  1163.962634   32.892113  484.159870  36.450837   
2013 Set    2.033796   876.446347   14.618253  345.709428  35.093342   
2014 Set    3.059217   945.812965   47.732322  265.906290  30.325145   
2015 Set    3.762781   932.665415   63.716526  400.275190  23.022251   
2016 Set   19.959427   778.828782   33.844189  541.642372  18.355865   
2017 Set    5.097770  1082.794121  100.344350  588.764766  25.892078   

BIOME_NUM      11.0       12.0        13.0        14.0  
2001 Set   0.028093  30.625437  147.242173    5.385856  
2002 Set   0.579421  21.309403  127.365858   57.979280  
2003 Set   3.122352  22.217026   38.661227   30.182481  
2004 Set   0.398822  32.137129   34.109964   22.736264  
2005 Set   0.349158  13.990746   22.521240   16.519034  
2006 Set   0.253340  25.428614   77.414878   50.027010  
2007 Set   2.121534  31.882145  144.079274    8.892084  
2008 Set   0.850319  27.198326   78.636879   11.856112  
2009 Set   0.140967  38.876000   16.841202   55.882772  
2010 Set   0.054180  17.964252   54.040929   12.470606  
2011 Set   0.025585  28.612882  392.616192   36.038243  
2012 Set   0.416380  32.571210  134.570485   29.170373  
2013 Set   0.272403  20.058766   22.884907   12.542899  
2014 Set   0.220230  25.061315   36.647194   39.616765  
2015 Set   0.428420  37.601079   25.200723  126.260365  
2016 Set   0.367719  30.786320    9.806956   11.386205  
2017 Set   0.137456  18.040140  134.035231    8.133004  
Out
BIOME_NUM      0.0        1.0         2.0       3.0        4.0        5.0   \
2001 Out   0.001387  33.984645   63.303647  0.249090  27.503074   2.988824   
2002 Out   0.000925  92.179191  123.950790  0.182666  18.431815   3.498946   
2003 Out   0.002466  87.248021   94.628375  0.000000  15.073780   3.750636   
2004 Out   0.001695  76.937281   63.581831  0.116242  24.932642   2.422520   
2005 Out   0.002466  70.848358  119.429538  0.464969  46.940500   2.222516   
2006 Out   0.002620  97.066869   63.749964  0.149454  14.935362   2.831517   
2007 Out   0.001079  93.341844  134.423955  0.016606  16.076623  14.937376   
2008 Out   0.000000  65.881658   76.124561  0.132848  19.790393   3.303437   
2009 Out   0.000617  70.221701   53.032242  0.016606  16.844846   2.564096   
2010 Out   0.002929  87.004219  124.308455  0.298908  26.783298   2.777583   
2011 Out   0.000617  54.040112   41.571677  0.282302  39.491505  11.624951   
2012 Out   0.003237  76.394546  104.805014  0.016606  15.914673   3.986596   
2013 Out   0.001387  42.950186   62.710595  0.016606  19.587609   1.950601   
2014 Out   0.003083  86.824124   71.872322  0.747271  45.635213   3.018038   
2015 Out   0.001079  98.924786  122.880852  0.066424  37.222133   3.355123   
2016 Out   0.000308  61.889555  118.069188  0.315514  18.074003   3.098938   
2017 Out   0.012485  63.255581   88.040614  0.365332  17.111302   3.964124   

BIOME_NUM      6.0          7.0         8.0         9.0        10.0      11.0  \
2001 Out   0.386861   815.557296   13.036188  485.267852   7.249867  0.011538   
2002 Out   0.925138   741.187762   74.974558  524.568080  16.578396  0.000000   
2003 Out   0.831593   760.323669  173.502369  314.638573  30.486347  0.142974   
2004 Out   0.181936  1104.017267   58.715830  420.152583  17.032308  0.006522   
2005 Out   3.848794   675.623718  129.482394  690.765430  19.991221  0.001505   
2006 Out   0.407076   630.683761   29.124694  266.332437  12.764686  0.003512   
2007 Out   2.075416   787.905408   54.210781  331.447707   7.572272  0.002508   
2008 Out   1.806674   738.215297   43.808032  511.007134  15.184844  0.001003   
2009 Out   0.068573   631.543933   35.360335  380.965994  12.334106  0.000000   
2010 Out   0.286182   637.623040   35.907717  411.459183  28.927350  0.000000   
2011 Out   2.225245   733.761331   31.554533  344.061659  19.815171  0.001003   
2012 Out   0.227519   672.721959   24.015523  230.536085   7.205324  0.001003   
2013 Out   0.265174   503.359359   23.729316  288.700422  21.229934  0.001003   
2014 Out   0.998070   738.201935   46.927104  302.583346  23.230117  0.000000   
2015 Out   0.590598   558.773372   42.068260  398.447493  20.385743  0.135951   
2016 Out   0.199773   642.144371    7.717577  592.751609   7.661357  0.007023   
2017 Out   0.120894   723.049544   23.725978  368.456211  28.800085  0.000000   

BIOME_NUM       12.0        13.0        14.0  
2001 Out   16.470772   85.292188    7.373923  
2002 Out   30.959345  244.736296   73.305474  
2003 Out   50.107452   48.849129   11.133178  
2004 Out   15.651180  127.542267   58.666067  
2005 Out   22.414335   35.092467   16.374448  
2006 Out   13.693264   95.253451  104.030154  
2007 Out   40.296627   74.290154    9.578871  
2008 Out   14.418755   50.108904   13.952620  
2009 Out   18.689743   34.821626   16.663621  
2010 Out   21.066561   98.507569   15.217754  
2011 Out   20.386603  376.927072    9.795751  
2012 Out   10.688092  327.186255   18.145635  
2013 Out   26.557830   47.790275   18.687835  
2014 Out   22.948588  115.005999   39.580618  
2015 Out   28.330578   99.578076   85.089292  
2016 Out   20.829790   20.329995   16.410594  
2017 Out   66.311098  105.064426    7.554657  
Nov
BIOME_NUM      0.0        1.0        2.0       3.0        4.0       5.0   \
2001 Nov   0.001695  32.765022  30.459607  0.730665   5.721531  0.242701   
2002 Nov   0.000000  42.109743  54.890877  0.780483  12.106087  3.285459   
2003 Nov   0.000000  45.118676  62.031949  0.116242   4.203079  1.973073   
2004 Nov   0.000000  56.242293  83.302317  0.963149   4.391329  2.510162   
2005 Nov   0.000000  73.544268  83.990134  0.415151   5.777590  2.312406   
2006 Nov   0.000308  46.163103  38.636990  0.348726  10.610474  0.573045   
2007 Nov   0.000771  40.735141  77.689728  0.083030  12.010578  1.471939   
2008 Nov   0.000000  40.598538  55.401390  1.643996   8.755666  1.759586   
2009 Nov   0.000000  56.155921  48.694407  1.295270   6.261363  1.793294   
2010 Nov   0.001233  39.582288  39.911745  2.275025  15.320165  1.426995   
2011 Nov   0.001079  40.401904  43.405857  0.265696  12.699210  1.561829   
2012 Nov   0.001695  36.463094  43.989737  0.199272   5.424623  1.197777   
2013 Nov   0.000000  32.659048  41.565563  0.863513   7.188768  2.555107   
2014 Nov   0.000000  33.488465  46.829658  0.498181  22.663270  0.878669   
2015 Nov   0.001541  68.564705  84.552616  0.464969  13.839779  0.793274   
2016 Nov   0.000154  36.569680  65.409896  1.378300   7.731368  0.961817   
2017 Nov   0.000154  34.774244  41.522766  0.298908   8.023431  3.116916   

BIOME_NUM      6.0          7.0        8.0          9.0       10.0  11.0  \
2001 Nov   0.000396   800.815227   2.544155   123.222787  0.434822   0.0   
2002 Nov   0.116930   684.890193  18.111982   972.666440  6.429007   0.0   
2003 Nov   0.000000   769.524448  14.947016   299.770776  4.019455   0.0   
2004 Nov   0.088391   981.341135   3.433650   617.970044  2.791347   0.0   
2005 Nov   0.099490  1107.032602   8.068869   403.646487  5.953884   0.0   
2006 Nov   0.002775  1009.874352   3.816650   386.373327  1.387189   0.0   
2007 Nov   0.091959   805.529750   4.673602   350.273936  2.861343   0.0   
2008 Nov   0.000000   888.503232   9.998889   347.859103  2.131690   0.0   
2009 Nov   0.156568   825.063173   6.930716  1548.362697  1.295983   0.0   
2010 Nov   0.000000   618.229916   5.807583   211.757205  6.422644   0.0   
2011 Nov   0.004756   671.572836   7.554864   484.699656  5.035455   0.0   
2012 Nov   0.000000   734.521846   6.865631   303.776558  3.033151   0.0   
2013 Nov   0.000000   583.802989   7.564043   344.904484  5.300590   0.0   
2014 Nov   0.000000   598.366900  12.508832   283.349909  1.667172   0.0   
2015 Nov   0.000000   727.556957   9.015938  1035.272177  8.745231   0.0   
2016 Nov   0.177972   744.282711   5.754180   735.075254  3.260107   0.0   
2017 Nov   0.002775   641.145570   6.818069   280.262711  8.420705   0.0   

BIOME_NUM       12.0        13.0       14.0  
2001 Nov    5.075402  103.090578   2.927881  
2002 Nov   63.445560  226.732565  11.458499  
2003 Nov   13.693264   23.873438   9.181258  
2004 Nov   10.023312   86.982738  20.314436  
2005 Nov   26.433373   26.898907   7.988417  
2006 Nov   28.974110  195.605475  23.748371  
2007 Nov   21.461179   47.199969  13.121246  
2008 Nov    7.151703   35.859584  11.711525  
2009 Nov   21.324580   44.666757   9.759604  
2010 Nov    9.983850   40.265381   4.735216  
2011 Nov   12.849388   77.105057   5.855763  
2012 Nov   11.662497  151.177011  11.892259  
2013 Nov   12.217998   52.933452   5.205122  
2014 Nov    6.201583   93.218121   9.181258  
2015 Nov   57.073988   91.097199  14.747847  
2016 Nov   25.817161   46.270509  13.771886  
2017 Nov   36.271518  130.560101   7.988417  
Dez
BIOME_NUM      0.0        1.0         2.0       3.0        4.0       5.0   \
2001 Dez   0.000000  27.441196   17.546983  0.232484  12.718589  0.168543   
2002 Dez   0.001233  49.350906   36.506285  0.448363   5.864102  0.294388   
2003 Dez   0.000000  63.737671   72.798582  1.594178   2.405022  0.525853   
2004 Dez   0.000000  56.472618  202.523981  3.487264   1.293521  0.660687   
2005 Dez   0.001079  28.440907   25.216911  1.976116   2.447932  1.379803   
2006 Dez   0.000000  59.290430   41.220126  0.464969  22.976788  0.869680   
2007 Dez   0.000925  46.620079   36.151677  1.660602   1.305979  0.361805   
2008 Dez   0.000771  38.321012   33.427921  0.315514   1.549596  0.083148   
2009 Dez   0.000000  50.198700   36.604108  0.315514   3.565662  0.188768   
2010 Dez   0.000000  54.009483   29.047289  1.843268   3.798205  0.404502   
2011 Dez   0.000000  63.150832   31.795501  3.570294   2.027140  0.979795   
2012 Dez   0.000925  43.851885   85.674522  1.942904   3.400252  0.301130   
2013 Dez   0.000000  37.214714   26.277678  0.581211   3.786440  1.977567   
2014 Dez   0.000000  33.865806   40.862461  1.594178   1.921250  0.328096   
2015 Dez   0.000000  63.341340  144.420233  1.345088   1.745458  1.155079   
2016 Dez   0.000000  32.838530   21.404874  3.038902   3.268754  1.341600   
2017 Dez   0.000000   0.000000    0.000000  0.000000   0.000000  0.000000   

BIOME_NUM      6.0          7.0        8.0          9.0        10.0  11.0  \
2001 Dez   0.000000  1148.667716  18.591775   493.478286   3.586754   0.0   
2002 Dez   0.000000  1334.297312   6.571080  2094.616927   4.397008   0.0   
2003 Dez   0.001585  1379.184378  13.287349  1189.565819   7.075938   0.0   
2004 Dez   0.000000  1051.410922   9.673464  1363.433816   5.069392   0.0   
2005 Dez   0.001585  1240.612042   5.959448  1131.022684   5.147872   0.0   
2006 Dez   0.012288  1278.806468   2.261286  1009.419256  17.602880   0.0   
2007 Dez   0.000000  1161.896551   2.668484  1172.624106   4.078845   0.0   
2008 Dez   0.000000  1055.165616   2.281312  1175.985933   4.920916   0.0   
2009 Dez   0.000000  1115.934409   0.766000  1142.916922   1.158112   0.0   
2010 Dez   0.000000  1062.367121   3.326844   744.942926   2.801953   0.0   
2011 Dez   0.000000  1131.710912   7.888633  1153.684237   7.235019   0.0   
2012 Dez   0.004756  1018.605795   2.444024  1107.186859   3.991881   0.0   
2013 Dez   0.000000   892.228974   5.115846   914.284294   2.473184   0.0   
2014 Dez   0.000000   923.814272   6.963258   778.135046   3.567664   0.0   
2015 Dez   0.000000   806.393262   3.884238  1914.820751   5.898736   0.0   
2016 Dez   0.000000  1010.840306  42.863465  1370.735136   4.015213   0.0   
2017 Dez   0.000000     0.000000   0.000000     0.000000   0.000000   0.0   

BIOME_NUM       12.0       13.0       14.0  
2001 Dez    4.198134  95.979580   3.036321  
2002 Dez   72.807126  60.272696  15.868394  
2003 Dez   12.639936  11.206974  28.953493  
2004 Dez   71.608093  42.905483  24.688185  
2005 Dez   25.841445   7.581956  10.916298  
2006 Dez   37.106288  35.691614  30.941561  
2007 Dez   16.874497  44.220310   9.578871  
2008 Dez    9.804754   4.761829  14.603260  
2009 Dez   16.807716  10.875856  11.675379  
2010 Dez    3.241943   6.160239  12.398312  
2011 Dez    5.852497  48.275299  16.627474  
2012 Dez    6.389785  20.822251  18.615542  
2013 Dez    9.404064  11.012884  27.001572  
2014 Dez    3.584958  17.314974  15.434634  
2015 Dez    7.066708  18.853225  32.387428  
2016 Dez   41.022118  12.427770  22.916997  
2017 Dez    0.000000   0.000000   0.000000

intra_biome

intra_biome[1]

Jan     806.170362
Fev    1351.098356
Mar    2169.837995
Abr     989.661960
Mai     424.426734
Jun     439.594524
Jul     799.173001
Ago    1590.655214
Set    2064.521677
Out    1258.992673
Nov     755.936134
Dez     748.146110
Name: 1.0, dtype: float64

De novo¶

intra_biome = [ b for a, b in biomes_pa.groupby(biomes_pa.index.get_level_values(level=1),sort=False)]

intra_biome

[BIOME_NUM      0.0        1.0         2.0        3.0        4.0       5.0   \
 2001 Jan   0.005549  27.050378  294.159587   2.457691   0.708703  0.235960   
 2002 Jan   0.000000  38.795751  314.488409   2.457691  10.087252  0.020225   
 2003 Jan   0.002929  69.941757  263.764176   1.942904  27.791677  1.289913   
 2004 Jan   0.000000  46.164329  356.735676   5.131260   1.690783  0.038203   
 2005 Jan   0.000000  81.842110  281.295875   7.339861   1.352349  0.379783   
 2006 Jan   0.004316  33.638544  229.140982   9.664704   6.664161  2.114649   
 2007 Jan   0.001387  59.109110  429.797157   2.457691   8.703067  1.114629   
 2008 Jan   0.000308  51.483254  265.298773  13.816209   0.824975  0.159554   
 2009 Jan   0.007861  38.517033  158.662637   6.293682   2.587043  1.373061   
 2010 Jan   0.000000  38.863134  300.401911   2.424479   1.015992  1.116876   
 2011 Jan   0.000154  33.209746  333.621958   4.931988   0.574437  0.858444   
 2012 Jan   0.000000  37.056059  265.622811   2.025934   2.193934  0.707879   
 2013 Jan   0.000462  49.828096  235.612579   4.965200   6.802580  0.979795   
 2014 Jan   0.000000  33.885409  170.587860   1.262058   8.274661  0.955075   
 2015 Jan   0.000000  55.159273  272.210573   2.291631   2.052055  1.155079   
 2016 Jan   0.000000  83.399258  207.277563   3.786173   4.299972  0.444953   
 2017 Jan   0.000462  28.227121   85.338256  21.471584  13.039720  0.961817   
 
 BIOME_NUM      6.0          7.0         8.0          9.0        10.0  11.0  \
 2001 Jan   0.054303   816.822779  136.908757  1317.788732   8.662509   0.0   
 2002 Jan   0.000000   772.452931   17.127363  1716.037356   5.985700   0.0   
 2003 Jan   0.000000   998.604149   24.766503   735.198363  69.471880   0.0   
 2004 Jan   0.000000   667.036472    6.575252  1290.950938   9.954249   0.0   
 2005 Jan   0.000000  1054.433495    7.387145  1131.155263   9.205507   0.0   
 2006 Jan   0.094337   703.938134    9.546631  1314.815173  14.864560   0.0   
 2007 Jan   0.000000   877.533672    2.635942  1228.657709  12.928010   0.0   
 2008 Jan   0.000000   840.320232    3.999389  1122.035716   5.707838   0.0   
 2009 Jan   0.000000   622.330905    9.943817  1177.311724   7.705900   0.0   
 2010 Jan   0.000000   770.405220    2.907129   932.210880   7.616814   0.0   
 2011 Jan   0.000000   722.594126    4.052792  1213.089136   9.807895   0.0   
 2012 Jan   0.000000   820.254560    7.760967  1352.808550   8.598876   0.0   
 2013 Jan   0.000000   849.253217    8.885769  1189.944617   6.095997   0.0   
 2014 Jan   0.007927   694.905492   29.568607  1221.185930   4.950611   0.0   
 2015 Jan   0.000000   674.351554   13.415016  1140.691487   6.643237   0.0   
 2016 Jan   0.000000   992.973780    3.777432  1048.236517   5.807529   0.0   
 2017 Jan   0.000000   504.634307   48.590944   988.850558   5.652690   0.0   
 
 BIOME_NUM       12.0        13.0        14.0  
 2001 Jan   87.917982  150.205357    6.904017  
 2002 Jan   21.373149   71.646836   30.182481  
 2003 Jan   37.652683   16.461863   54.364612  
 2004 Jan    4.295271    9.402703   19.989116  
 2005 Jan   42.151334   11.301005  100.668513  
 2006 Jan   20.310714    1.830793   15.109314  
 2007 Jan    8.769639    5.669590   39.580618  
 2008 Jan   21.600813   30.440342   46.340048  
 2009 Jan   21.464215    4.134955   34.773109  
 2010 Jan   23.294638    5.230377   25.555705  
 2011 Jan    5.706792   12.747235   29.170373  
 2012 Jan   16.276498   40.269399   40.195112  
 2013 Jan    7.804341   30.202451   16.808208  
 2014 Jan   58.476401    8.624335   14.711700  
 2015 Jan   11.122173   11.515187   69.112458  
 2016 Jan   30.476696    9.644612   38.532364  
 2017 Jan   27.574732    2.441994   29.061933  ,
 BIOME_NUM      0.0         1.0         2.0        3.0        4.0       5.0   \
 2001 Fev   0.000000   59.446635  130.801451   8.435858   0.742616  0.208993   
 2002 Fev   0.003391   42.650640  112.967112   3.819385   3.579504  2.546118   
 2003 Fev   0.000000   50.174197  229.266318   8.286404   8.082951  1.269688   
 2004 Fev   0.000000  113.045085  178.939490   8.136950   3.245915  0.921367   
 2005 Fev   0.001387  107.077450  143.643764  16.340324   2.214697  1.806778   
 2006 Fev   0.000000   52.210985  265.097013  21.886734   3.701312  1.543851   
 2007 Fev   0.002158  110.519468  119.723007   5.197684   5.190696  2.851742   
 2008 Fev   0.002312   74.455769  164.935530   6.559378  17.835231  3.591083   
 2009 Fev   0.000000  120.912905  306.726773  42.710683  12.481893  2.791067   
 2010 Fev   0.000000   94.073863   79.355774   7.638769   2.551746  2.905676   
 2011 Fev   0.000771   49.249832  149.883031  14.380813   4.991373  0.723610   
 2012 Fev   0.002004  104.831164  157.024103  18.067350   6.722297  1.182046   
 2013 Fev   0.002774   71.312684  160.668006  15.410387   4.876486  2.449487   
 2014 Fev   0.000154   68.994115  188.003394   4.201323  15.160292  2.487690   
 2015 Fev   0.000000   61.403788  179.355238   8.469070  10.694910  1.022492   
 2016 Fev   0.000000   94.105716  147.737041  17.868078   5.158859  1.487670   
 2017 Fev   0.002620   76.634060  324.906548  24.942242   5.839187  3.301189   
 
 BIOME_NUM      6.0         7.0        8.0          9.0        10.0  11.0  \
 2001 Fev   0.000000  317.193617  20.659474   510.126430   8.036789   0.0   
 2002 Fev   0.001189  357.576610  21.891082  1204.121108   5.519062   0.0   
 2003 Fev   0.008324  377.099455  21.467196   400.701337  18.175573   0.0   
 2004 Fev   0.000000  479.696553   4.898896   776.989184   7.016547   0.0   
 2005 Fev   0.046772  365.888823  11.448281   536.149810   5.930552   0.0   
 2006 Fev   0.000396  266.026460   7.617446   618.604530   7.642267   0.0   
 2007 Fev   0.000000  470.997401   7.161851  1031.948230  13.456160   0.0   
 2008 Fev   0.042808  379.819157   9.573333   987.373248   7.472581   0.0   
 2009 Fev   0.000000  257.423068   6.117154   777.737309  10.439978   0.0   
 2010 Fev   0.077689  308.803459   3.132423   380.615606   6.129934   0.0   
 2011 Fev   0.000000  394.403668  10.406921  1092.981957   5.190294   0.0   
 2012 Fev   0.001189  390.678482   4.648569   893.109520   5.597542   0.0   
 2013 Fev   0.000000  343.137410   5.544740   604.304929   5.018486   0.0   
 2014 Fev   0.005946  360.093100   6.239814   714.430797   2.948307   0.0   
 2015 Fev   0.017837  308.463287   3.942648   920.098834   6.204172   0.0   
 2016 Fev   0.000000  290.776033   9.225379   373.768844   2.536817   0.0   
 2017 Fev   0.072140  273.661670  18.064419   526.282139   2.203807   0.0   
 
 BIOME_NUM       12.0       13.0        14.0  
 2001 Fev   11.783918   6.511449   62.714495  
 2002 Fev   15.751352  21.735638   72.835567  
 2003 Fev   46.558920   4.743344   66.039990  
 2004 Fev    8.642147   3.420480  124.163857  
 2005 Fev   47.172097  11.537288  153.514964  
 2006 Fev   11.908374   4.948685   96.692378  
 2007 Fev    9.695475  22.647016  134.718689  
 2008 Fev    8.733213   6.416213   98.246685  
 2009 Fev   10.047596   3.844021  104.897675  
 2010 Fev   69.862665   7.766402  128.248432  
 2011 Fev    6.274435  14.488818  109.018397  
 2012 Fev   13.034555  36.203963   89.173868  
 2013 Fev    8.241457   7.104568   80.173343  
 2014 Fev    5.907136   7.041881  129.079806  
 2015 Fev   46.901935   6.745321   98.318978  
 2016 Fev   12.211927   4.665387  102.403554  
 2017 Fev   14.904440   5.218321   74.389874  ,
 BIOME_NUM      0.0         1.0         2.0         3.0        4.0        5.0   \
 2001 Mar   0.000000   44.769512   57.736912   34.308037   4.959537   1.622504   
 2002 Mar   0.000617   43.605021   71.166163   42.528017  34.784582  13.964323   
 2003 Mar   0.000308  141.255666  131.024609  156.827253  45.210960  25.045442   
 2004 Mar   0.000308  220.293442  180.354866   84.740520   8.633857   3.462990   
 2005 Mar   0.000925  104.012774  116.048534   96.680248  19.184811   7.764200   
 2006 Mar   0.000154  128.668012   70.255187   87.596756  13.741502   5.892252   
 2007 Mar   0.000771  205.113401  105.202419   32.547799  30.962846   6.795641   
 2008 Mar   0.000771  103.620118  127.454073   55.862651  80.434329  68.792381   
 2009 Mar   0.001541  125.883892  141.947148   52.358781  26.440020   9.977727   
 2010 Mar   0.000000  227.919910  146.734356   73.249154  16.511949   9.132766   
 2011 Mar   0.000000   98.516816  141.485546  114.083357  25.596359  14.618269   
 2012 Mar   0.016492  159.148157  127.753656   47.260733  33.266823  15.013782   
 2013 Mar   0.000308   88.807005  102.169909  104.152958   6.107719   4.986616   
 2014 Mar   0.001233  140.983074   85.124268   18.017532  29.973154  19.959948   
 2015 Mar   0.001387  156.261738   85.625611   44.736618  24.295224  11.406969   
 2016 Mar   0.000000   86.784919   87.951962   32.514587  17.540399   8.584441   
 2017 Mar   0.000154   94.194538  140.553171   79.526230  45.869832  10.694595   
 
 BIOME_NUM       6.0         7.0        8.0         9.0       10.0      11.0  \
 2001 Mar    0.003567  145.483777  15.521933  320.159545  3.064967  0.000000   
 2002 Mar    1.120946  153.289352  17.100661  591.558397  2.814679  0.000000   
 2003 Mar   31.788821  174.533097  32.840379  586.880249  4.503062  0.318556   
 2004 Mar    0.242977  217.509412  16.932108  441.422056  2.536817  0.000000   
 2005 Mar   11.667674  178.737084  43.949049  510.079081  1.921703  0.004013   
 2006 Mar    0.395582  167.592705  26.106586  453.363643  2.328951  0.000000   
 2007 Mar    2.539570  207.076554  41.380696  526.736695  8.301925  0.005518   
 2008 Mar    7.724938  167.692920  22.906575  744.639888  4.976064  0.011538   
 2009 Mar    4.075124  145.991529  20.865577  503.819454  4.017334  0.000000   
 2010 Mar    0.209286  168.825340  11.843797  271.938638  2.131690  0.000000   
 2011 Mar   20.190513  153.826054  26.959367  573.366653  6.250836  0.015050   
 2012 Mar    2.638267  149.672731  19.966069  683.984957  7.778017  0.000502   
 2013 Mar    0.057871  143.947159   9.579174  396.828134  2.477427  0.000000   
 2014 Mar   41.697782  135.308692  90.566583  447.871081  2.015030  0.000000   
 2015 Mar   12.897623  146.950802  51.473874  477.701375  6.706869  0.028093   
 2016 Mar    1.627513  102.840953  29.753014  152.816333  1.628993  0.000502   
 2017 Mar    3.043362  111.447128  58.855179  548.839522  2.218654  0.049163   
 
 BIOME_NUM       12.0       13.0        14.0  
 2001 Mar   13.559701   9.062745   85.992960  
 2002 Mar   15.317272  14.328885   77.064729  
 2003 Mar   28.200050   8.927324  230.182079  
 2004 Mar   17.250903   5.462641  185.432485  
 2005 Mar   21.394398   9.696048  160.310540  
 2006 Mar   18.413510   8.031216  242.327365  
 2007 Mar    7.248840  25.590911  108.114730  
 2008 Mar    8.074503   9.986981   99.981726  
 2009 Mar   25.000604   8.004694  140.393718  
 2010 Mar   24.481529   5.185772  152.864324  
 2011 Mar   31.800186   7.309106  111.910131  
 2012 Mar   22.074356   9.286571  153.153497  
 2013 Mar    7.136525   9.044662  171.660599  
 2014 Mar   24.262971   9.961263  199.493545  
 2015 Mar   16.133829  15.651347  136.887490  
 2016 Mar   11.213238   6.606284  151.816070  
 2017 Mar   56.041908   7.587582  131.031727  ,
 BIOME_NUM      0.0        1.0         2.0         3.0         4.0        5.0   \
 2001 Abr   0.000000  23.675130   52.096810  137.597482   21.573224   6.919239   
 2002 Abr   0.000000  32.785237   50.562214   98.440487   14.291024  10.946285   
 2003 Abr   0.002620  68.361945   64.064831  141.184382   39.548257   7.528240   
 2004 Abr   0.000000  42.651253   70.943004  103.173202   39.675602   2.991071   
 2005 Abr   0.000617  59.959354   96.459497  232.783189   31.331040   4.752904   
 2006 Abr   0.000000  43.317114   51.671892  156.246042   68.094315  13.328355   
 2007 Abr   0.000308  75.180436   74.580793   82.199799   16.161058  16.290212   
 2008 Abr   0.000617  44.047907   91.161776  204.818651  103.763392  89.066942   
 2009 Abr   0.001233  82.919617   91.901561  281.986826  122.585547  12.108106   
 2010 Abr   0.000000  59.825814   74.849806   78.529869   38.862393  17.119442   
 2011 Abr   0.000000  36.284836  116.941168  521.844179   53.620579  22.697081   
 2012 Abr   0.000462  37.726821   94.356305  172.719214   47.346066  17.256523   
 2013 Abr   0.000154  83.766798   89.376508  170.859340   40.559404  63.565310   
 2014 Abr   0.000000  83.349640   89.116666   85.753487   72.750023   9.141755   
 2015 Abr   0.000000  62.071487   77.032480   66.739594   32.100647  12.296874   
 2016 Abr   0.000000  93.673856  107.751929  267.655831   24.626737  10.206945   
 2017 Abr   0.000308  60.064715  157.430679  259.585305   56.259529   0.991031   
 
 BIOME_NUM       6.0         7.0         8.0         9.0       10.0      11.0  \
 2001 Abr    2.560974  132.973701   42.203437  289.126569  0.702079  0.000000   
 2002 Abr    3.412783  144.868016   23.242847  426.961466  2.555907  0.000000   
 2003 Abr   12.111613  118.742724   74.783476  536.263449  3.637660  0.024080   
 2004 Abr    7.481961  107.863913   60.563242  291.702392  1.187807  0.022073   
 2005 Abr    5.500090  190.885832   67.004152  349.487932  1.187807  0.000502   
 2006 Abr   20.666955  106.652991   42.204271  387.235091  0.863281  0.000000   
 2007 Abr    7.538246  113.796038   34.375717  388.608231  2.163506  0.000000   
 2008 Abr   23.620736  131.826805  182.177863  547.002355  4.766077  0.016555   
 2009 Abr   15.439175  186.535978   76.559127  592.798958  1.756258  0.004013   
 2010 Abr   12.617386  104.058556   65.004040  171.860371  1.989577  0.005017   
 2011 Abr   19.048955   91.315203  106.399756  409.356858  1.639598  0.054180   
 2012 Abr    6.084742  172.914638  132.970281  305.414857  4.978185  0.005518   
 2013 Abr    8.367461  145.995426   16.242040  462.985097  1.580208  0.095316   
 2014 Abr    8.024201   88.906722   95.020732  628.699480  4.923037  0.011538   
 2015 Abr    4.339506  177.553443  108.767013  421.563603  5.130903  0.008027   
 2016 Abr    3.311311  130.202221   56.154986  243.812933  1.022363  0.006522   
 2017 Abr    9.359982   97.633710   67.168533  379.753842  1.677778  0.003010   
 
 BIOME_NUM       12.0       13.0        14.0  
 2001 Abr   20.769079  42.457831   64.666416  
 2002 Abr   18.349764  46.175674   43.448313  
 2003 Abr    9.224968  12.533053  172.347386  
 2004 Abr   60.792509   8.747299  134.646395  
 2005 Abr   25.131132  12.570022   87.511120  
 2006 Abr   23.649795   8.800744   79.125090  
 2007 Abr    9.255323   5.773667   87.547267  
 2008 Abr   23.668008  11.710482   77.968396  
 2009 Abr   38.812254  10.966672  198.842905  
 2010 Abr   32.486215   6.714379   74.426021  
 2011 Abr   24.876148  30.975997   82.992785  
 2012 Abr   27.814538  32.742897   87.294240  
 2013 Abr   20.857109  10.393244  117.187547  
 2014 Abr   51.069714   9.424804  149.683415  
 2015 Abr   30.343133  27.108267  232.423173  
 2016 Abr    9.100511  16.272997  144.478293  
 2017 Abr   46.762301  11.181658  108.620783  ,
 BIOME_NUM      0.0        1.0         2.0         3.0        4.0        5.0   \
 2001 Mai   0.000000   9.948100   58.293280  194.406676   1.519144   2.179819   
 2002 Mai   0.000154  21.687960   48.083014  259.369427  17.277404  18.759924   
 2003 Mai   0.000617  53.719126  100.271534  371.842000  42.043943  91.251255   
 2004 Mai   0.000771  25.977772   47.422710   90.004628  42.529100   4.678745   
 2005 Mai   0.000308  32.214936   89.235887  434.197605  27.980618   3.242761   
 2006 Mai   0.000771  18.705368   52.842710  259.469063  63.459370   6.301249   
 2007 Mai   0.000154  27.569223   63.820274  304.786891  41.387840   6.020345   
 2008 Mai   0.000617  22.302366   78.536507  332.236642  29.508067  17.622823   
 2009 Mai   0.000308  20.860381   63.083545  189.673961  52.255080  11.227190   
 2010 Mai   0.000000  23.389673   54.517927  117.969166   5.717378   2.804550   
 2011 Mai   0.000000  24.325677   53.622236  578.952282  23.468174  22.355501   
 2012 Mai   0.000308  17.802443   56.975728  396.020365  31.780900  72.657626   
 2013 Mai   0.000154  28.638154   80.835345  255.749314  33.235679   4.739420   
 2014 Mai   0.002466  21.675709   37.276640   77.998476  22.227943   6.566423   
 2015 Mai   0.000000  23.839911   38.441344   85.022822   4.246681   4.089969   
 2016 Mai   0.000000  34.980680   67.895209  157.275616  20.716413  20.085793   
 2017 Mai   0.000000  16.789256   54.548497  202.360960   6.187309   2.905676   
 
 BIOME_NUM       6.0         7.0         8.0         9.0        10.0      11.0  \
 2001 Mai    1.100731  360.944364   11.273052   95.712628   6.766259  0.000000   
 2002 Mai    7.086776  436.149574   22.342505  332.981836  26.929288  0.170064   
 2003 Mai   65.871461  489.978533  116.057365  307.128915  22.721057  1.783413   
 2004 Mai   24.138401  404.860466   75.526112  175.809334  20.389985  0.070735   
 2005 Mai    3.569747  412.360944   18.318084  403.068821  20.928740  0.254845   
 2006 Mai    6.422056  257.083453   96.340789  271.645070  28.734331  0.021572   
 2007 Mai    6.053032  373.176624   42.794208  284.902979  25.073339  0.055685   
 2008 Mai   20.122733  338.210767   56.065703  134.009043   4.121267  0.165047   
 2009 Mai    6.109713  232.606685   34.672771  359.885920   5.489367  0.137957   
 2010 Mai    2.485267  265.965217   18.344785  101.707096   5.934794  0.107356   
 2011 Mai    6.181853  313.276354   25.096935  229.522802  12.015944  0.134446   
 2012 Mai    7.674599  409.334474   38.753933  204.408536  22.899228  0.078259   
 2013 Mai    1.395237  340.854752   29.480992  400.909676  19.499129  0.312536   
 2014 Mai    7.249686  273.713447   30.131008  122.389433   5.296348  0.119898   
 2015 Mai    8.132808  304.752020   24.959255  171.244825  15.628150  0.246317   
 2016 Mai   15.270716  270.625179   35.902710  334.165578  14.576092  0.365211   
 2017 Mai    2.308484  323.876235   23.780216  126.234227  10.959643  0.007023   
 
 BIOME_NUM       12.0       13.0        14.0  
 2001 Mai    2.653051  42.684068   25.989465  
 2002 Mai    5.497340  34.714736   44.677301  
 2003 Mai    6.914932  14.200697  126.585685  
 2004 Mai   32.977970  23.333363   91.161935  
 2005 Mai   10.499890  22.835078   45.364088  
 2006 Mai   13.590056  17.035292  150.984696  
 2007 Mai    5.375919  10.286756  120.838362  
 2008 Mai    4.389373  14.379115   80.751690  
 2009 Mai   23.400881  12.101876   76.124915  
 2010 Mai   13.483813  11.582294   75.835742  
 2011 Mai    5.855532  33.761566  135.911529  
 2012 Mai   28.670557  20.209844   30.688534  
 2013 Mai   13.386676  19.002710   72.944007  
 2014 Mai    8.195924   8.763372   72.329513  
 2015 Mai   13.444351  17.339888   53.894705  
 2016 Mai    5.454843  17.217326   86.065253  
 2017 Mai   29.593357  31.725031   39.508325  ,
 BIOME_NUM      0.0        1.0        2.0         3.0        4.0        5.0   \
 2001 Jun   0.012331  46.332785  43.506737    3.371022   7.151395   3.089949   
 2002 Jun   0.005086  18.960196  23.932985   53.238900  17.788168  41.270484   
 2003 Jun   0.001541  29.766503  26.632286   85.238701  27.395108  13.573304   
 2004 Jun   0.003699  30.088101  29.817644   15.842143  23.683414   6.460803   
 2005 Jun   0.002004  24.963360  29.435523  181.204890  47.078918  13.600271   
 2006 Jun   0.003083  17.581919  27.378185   32.647435  84.112802   7.723749   
 2007 Jun   0.004778  32.542660  38.098964   65.676809  48.413965   3.961877   
 2008 Jun   0.002774  25.039318  32.599483   57.506647   4.985836  10.851901   
 2009 Jun   0.001387  30.837884  37.481457   35.719549  22.629357   5.991131   
 2010 Jun   0.000617  21.445996  27.992635   50.615149  78.823829   4.804590   
 2011 Jun   0.000462  20.926538  28.069060  197.130064  44.138908  30.991628   
 2012 Jun   0.011560  25.611456  26.543634  122.502610  97.373992  24.521836   
 2013 Jun   0.000771  32.344801  28.136313   70.774857  29.009760  13.393525   
 2014 Jun   0.002774  18.479943  28.582630  105.863378  25.660723   7.397900   
 2015 Jun   0.001695  25.143455  25.177170    7.921072  16.087696   8.759725   
 2016 Jun   0.002158  22.538817  27.796990   60.528943   3.630027  13.440717   
 2017 Jun   0.026049  16.990791  24.217283  191.616865  12.124773  13.229477   
 
 BIOME_NUM       6.0         7.0         8.0         9.0        10.0  \
 2001 Jun    4.647092  726.834858   40.303456  405.066977  49.474295   
 2002 Jun   19.215829  479.351371   30.773513  481.091611  22.337141   
 2003 Jun   39.553794  516.877145   24.638837  435.361298  22.731662   
 2004 Jun    9.640219  686.265912  114.286720  512.598083  21.582034   
 2005 Jun   15.339289  666.317713   47.410235  678.767023  34.444291   
 2006 Jun   17.903037  565.690938   96.106316  459.708499  25.525130   
 2007 Jun   11.965747  562.908323   68.961708  690.481332  18.854320   
 2008 Jun   21.725671  560.376243   61.024678  632.212826  28.662214   
 2009 Jun    7.481168  553.797736   22.770564  732.594131  29.531859   
 2010 Jun   17.815042  571.040707   99.994726  574.616684  34.297936   
 2011 Jun   21.578616  592.646895   57.168810  530.325801  34.722153   
 2012 Jun   40.369928  737.452555   33.136599  831.261378  24.464588   
 2013 Jun   24.323508  633.827704   13.668680  761.202803  14.569729   
 2014 Jun    8.870857  589.431131   31.023840  488.127773  24.297023   
 2015 Jun   35.863153  515.917315   41.262208  545.790204  20.739964   
 2016 Jun   13.304699  585.744361   13.601092  758.920549  16.287808   
 2017 Jun   10.505900  572.259980   34.535091  345.463210  20.595730   
 
 BIOME_NUM       11.0       12.0        13.0       14.0  
 2001 Jun    0.206685  14.910511   37.437618   2.385681  
 2002 Jun    0.892459  17.296436   15.433147   6.940163  
 2003 Jun    4.474334  22.478081    7.319955  21.037370  
 2004 Jun   17.104708  24.208332  101.585679  36.472003  
 2005 Jun    2.939746  20.219648   30.920141  24.832771  
 2006 Jun    0.163041  14.391436   70.654286  30.363214  
 2007 Jun    1.069546  19.096503   22.022955  57.328640  
 2008 Jun    0.657179  23.625510   15.253926  20.531316  
 2009 Jun    0.677747  22.365766   14.906332  27.037719  
 2010 Jun    1.528066  19.655040   41.327449  37.881724  
 2011 Jun    0.349158  13.077052   23.037607  33.218802  
 2012 Jun    1.951971  21.169769   27.853282  17.314261  
 2013 Jun    0.722897  16.091331    8.076222  19.374622  
 2014 Jun    0.199160  18.316373   12.800278  22.447090  
 2015 Jun    2.782224  34.462343   18.921538  21.615717  
 2016 Jun    2.212335  24.126372    3.559517  25.953319  
 2017 Jun    1.250646  38.171758   36.515390  17.928755  ,
 BIOME_NUM      0.0        1.0        2.0        3.0        4.0        5.0   \
 2001 Jul   0.025586  27.403217  23.248225   0.016606  25.104973   4.184353   
 2002 Jul   0.011868  40.711863  40.098220  13.185180  24.835749  36.196226   
 2003 Jul   0.008323  56.345204  45.426511   4.965200   6.426773  16.953146   
 2004 Jul   0.013872  46.576587  43.531192   0.730665  10.345403  11.406969   
 2005 Jul   0.007090  69.533787  40.932771   6.111015  14.345007  10.678864   
 2006 Jul   0.008786  35.078691  49.522845   0.016606  14.244653  19.993656   
 2007 Jul   0.061962  54.681470  45.594644   1.046179  20.581455  30.643307   
 2008 Jul   0.005703  40.465611  53.279856   0.215878  22.499244  23.285857   
 2009 Jul   0.004932  53.434895  28.897497   2.324843  12.885383   3.687714   
 2010 Jul   0.002774  48.006320  49.672637   0.232484  26.135499   6.069784   
 2011 Jul   0.002929  47.389464  30.887582   4.732716   5.042588   4.979874   
 2012 Jul   0.007553  51.647422  47.019190   0.481575   9.053958  11.597984   
 2013 Jul   0.004624  46.532482  29.151225   0.232484   4.751909   9.779970   
 2014 Jul   0.001850  37.260657  27.289533   0.265696   6.905702  32.270305   
 2015 Jul   0.009865  49.177549  28.506206   3.121932  11.771806  29.641040   
 2016 Jul   0.006628  50.751236  51.726918   1.295270   8.793731   6.872047   
 2017 Jul   0.015876  44.176546  27.524920   0.813695   6.546505  57.333726   
 
 BIOME_NUM       6.0         7.0         8.0         9.0        10.0  \
 2001 Jul   32.171323  530.560284  161.060291  787.093604  26.263268   
 2002 Jul   57.357341  814.174340  111.809319  883.222909  69.032815   
 2003 Jul   34.327202  846.399895   31.670518  741.884137  24.683060   
 2004 Jul   28.758144  818.818713   60.663373  781.222245  29.005830   
 2005 Jul   26.066366  962.167370  141.452189  676.816217  32.274421   
 2006 Jul   36.565528  776.525525  112.990862  807.861170  32.369870   
 2007 Jul   18.372345  971.532946   82.919932  751.742339  57.710466   
 2008 Jul   39.238676  781.773410  124.001905  780.474120  31.118430   
 2009 Jul   22.155341  797.223660  100.269251  574.872372  22.568339   
 2010 Jul   26.419139  812.357679   86.846726  810.863139  33.508892   
 2011 Jul   34.131393  932.011239   32.047677  873.809794  25.728755   
 2012 Jul   85.249011  837.244212   82.343346  663.009053  20.739964   
 2013 Jul   64.322827  765.446286   13.370791  555.459007  25.370291   
 2014 Jul   68.978639  789.900227   78.223801  810.086605  30.893595   
 2015 Jul   51.616652  877.116669   55.152845  834.954653  26.581431   
 2016 Jul   39.127295  784.563820   49.426201  614.863906  18.928558   
 2017 Jul   25.785733  718.447484  101.440781  719.885479  26.687485   
 
 BIOME_NUM       11.0       12.0        13.0       14.0  
 2001 Jul    3.521676  20.975495   42.202259   2.349534  
 2002 Jul    1.341447  33.026539   72.953225  16.193714  
 2003 Jul   16.025631  35.318362   13.975666  26.097905  
 2004 Jul   11.108830  35.139266   22.263659  22.627824  
 2005 Jul    3.118338  30.759000  100.478604  28.339000  
 2006 Jul    2.398953  20.250004   36.399258  17.061235  
 2007 Jul    1.543618  50.526355   39.236263  21.868743  
 2008 Jul    0.894967  21.312438   14.471941  15.868394  
 2009 Jul    3.174525  38.542092   23.747260  22.591677  
 2010 Jul   18.182281  19.472909   46.229521  14.386380  
 2011 Jul    1.311849  11.377157   40.657578  22.121770  
 2012 Jul    1.666525  36.119741   30.464854  16.880501  
 2013 Jul    1.603817  14.628207    8.517043  39.291445  
 2014 Jul    0.648149  13.708442   19.519881  33.038069  
 2015 Jul    3.372682  29.053033   22.235128  19.699942  
 2016 Jul    7.814909  19.582188    9.605633  14.422527  
 2017 Jul    5.416959  48.623079  102.125755  27.543773  ,
 BIOME_NUM      0.0         1.0         2.0       3.0        4.0        5.0   \
 2001 Ago   0.021425   82.596794   94.099520  0.000000  50.715174  17.067755   
 2002 Ago   0.008632  116.581438  106.217332  1.129209  54.015764  34.587205   
 2003 Ago   0.005857   99.114070   83.250348  0.116242  10.890772  41.454758   
 2004 Ago   0.000617  101.266633  117.521991  0.298908  37.110014   8.004654   
 2005 Ago   0.011868  154.063845  128.780796  0.398544  33.324959  12.249682   
 2006 Ago   0.033293   98.499664  122.617953  0.381938  24.237089  24.634198   
 2007 Ago   0.025586  123.725203  125.656577  0.000000  33.171314  54.048267   
 2008 Ago   0.008323   83.138304   58.797679  2.922660  74.969565  10.694595   
 2009 Ago   0.004316   62.814532   42.855603  1.627390  39.596703  20.627377   
 2010 Ago   0.012177  157.809696  248.363183  0.000000  36.853940  23.919577   
 2011 Ago   0.009402   71.233663   66.758018  0.083030  18.042858  11.042916   
 2012 Ago   0.031752   83.709217  113.544879  0.000000   9.152235  47.502069   
 2013 Ago   0.005241   69.721233   53.539698  2.773205  17.278788  36.384994   
 2014 Ago   0.006782   67.180302   53.408249  0.963149  11.391155  36.032178   
 2015 Ago   0.003545   75.837109   81.257207  0.714059  17.249720  81.147683   
 2016 Ago   0.010173   78.819089  110.866977  3.653324  19.042240  16.083466   
 2017 Ago   0.015568   64.544423   54.050211  0.631029  11.328866  88.520863   
 
 BIOME_NUM       6.0          7.0         8.0          9.0        10.0  \
 2001 Ago   17.046473  1240.009087  187.251154  1231.583918  54.378242   
 2002 Ago   46.529981  1027.067217  222.770864   974.996044  52.233826   
 2003 Ago   28.845742  1136.837427   49.532172   723.076847  63.884943   
 2004 Ago   15.795912  1029.077625   82.954978   888.279854  39.469140   
 2005 Ago   20.119166  1069.821389   86.905970  1124.602069  44.443083   
 2006 Ago    5.837008  1111.337917  154.950647  1027.279552  50.908148   
 2007 Ago    5.538934  1165.708589  129.941327   900.979036  33.860992   
 2008 Ago    4.886106   904.596523  218.920003   667.081124  53.924330   
 2009 Ago   11.313711   994.818835   52.012911   685.253929  51.317518   
 2010 Ago   21.872726  1256.384091  117.253928  1065.945295  55.977540   
 2011 Ago    8.148266  1067.495306   50.418329   519.511135  67.573509   
 2012 Ago   30.127617  1088.513013   66.778023   750.653296  44.638223   
 2013 Ago   40.857072  1027.416853   23.762693   415.701714  40.113949   
 2014 Ago   33.040175   977.326442  114.043069   537.646060  52.363212   
 2015 Ago   14.210811  1004.760086  138.258019   645.641193  31.228727   
 2016 Ago    5.492955   961.328354   62.429846   467.663244  28.235876   
 2017 Ago   40.606564   884.540316  113.463145   663.113222  36.071162   
 
 BIOME_NUM       11.0       12.0        13.0       14.0  
 2001 Ago    3.269339  35.227296  119.378443  10.590978  
 2002 Ago    8.248848  23.753002  190.515342  51.545170  
 2003 Ago   13.101436  85.550271   24.673908  16.446741  
 2004 Ago   12.954449  24.323682   17.791156  33.074215  
 2005 Ago    5.107433  38.080692   19.368788  27.724506  
 2006 Ago    1.594286  16.674152   64.809895  29.531840  
 2007 Ago    4.182366  90.176415   48.029773  13.012806  
 2008 Ago    1.853144  27.347067   69.790728  16.374448  
 2009 Ago    2.638748  41.774929   16.531382  41.460246  
 2010 Ago    8.679777  21.773839   67.479332   7.410070  
 2011 Ago    1.060516  20.377496  153.561140  37.773284  
 2012 Ago    2.208823  57.608240   61.137460  34.917696  
 2013 Ago    2.466176  26.815850   10.589343  36.652737  
 2014 Ago    0.844299  27.189220   33.595606  32.025961  
 2015 Ago    4.995562  25.128096   44.712165  43.845927  
 2016 Ago    8.049688  32.039992   16.054797  31.989815  
 2017 Ago    0.864867  44.813492   75.250154   8.530617  ,
 BIOME_NUM      0.0         1.0         2.0       3.0        4.0        5.0   \
 2001 Set   0.002774   60.699336   81.425340  0.531393  34.585260  10.092336   
 2002 Set   0.002158  134.690165   92.635233  0.033212  46.525244  56.965179   
 2003 Set   0.002774  112.037411   99.085431  0.531393  23.748471  12.501372   
 2004 Set   0.002158  141.682626  278.455954  0.049818  34.511206   2.800056   
 2005 Set   0.005549  163.125559  143.059883  0.000000  27.800674   6.793394   
 2006 Set   0.005549  136.191569  105.954433  0.116242  26.828976  16.283470   
 2007 Set   0.001541  211.957620  303.079813  0.016606  21.596063  16.231784   
 2008 Set   0.006628  101.886551  135.065918  0.431757  41.672290   5.775396   
 2009 Set   0.002620  100.949323   78.496767  0.614423  35.068340   8.676577   
 2010 Set   0.002158  185.279683  236.126149  0.033212  29.164789   7.382169   
 2011 Set   0.012793  112.613225  142.717503  0.763877  37.641541  16.429541   
 2012 Set   0.012639  102.774162  170.019264  0.033212  20.890128  22.721801   
 2013 Set   0.004778   77.650309   73.419147  1.693814  19.818076   5.258532   
 2014 Set   0.003083   85.466674   82.036733  0.016606  33.822573  14.780070   
 2015 Set   0.010481  117.677322  155.248006  0.016606  43.558242  19.568929   
 2016 Set   0.000771   83.842144   86.081099  0.415151  24.799760   8.445112   
 2017 Set   0.038688  135.997998  130.547723  0.033212  14.984500  44.758194   
 
 BIOME_NUM       6.0          7.0         8.0         9.0        10.0  \
 2001 Set    2.031418  1041.333825   59.118856  602.505641  41.957172   
 2002 Set   11.333530   846.622593  330.111849  554.654062  54.346426   
 2003 Set   13.546884   804.655102  101.769544  393.835635  32.446229   
 2004 Set    1.708770  1207.745116   65.342816  794.840010  37.352297   
 2005 Set    3.478581   930.333207  108.318094  658.491894  56.789915   
 2006 Set    6.130325  1066.804384  129.574181  488.118303  35.326662   
 2007 Set    5.014927  1253.876509  113.957958  709.506431  26.099944   
 2008 Set    1.744840  1183.549505   79.214261  633.197699  46.761428   
 2009 Set    4.239223   990.205640   31.184884  551.813082  38.663128   
 2010 Set    1.388103  1241.331358   96.044569  620.422757  44.856694   
 2011 Set    5.684800  1325.134391   54.897511  443.855829  39.252789   
 2012 Set    4.175803  1163.962634   32.892113  484.159870  36.450837   
 2013 Set    2.033796   876.446347   14.618253  345.709428  35.093342   
 2014 Set    3.059217   945.812965   47.732322  265.906290  30.325145   
 2015 Set    3.762781   932.665415   63.716526  400.275190  23.022251   
 2016 Set   19.959427   778.828782   33.844189  541.642372  18.355865   
 2017 Set    5.097770  1082.794121  100.344350  588.764766  25.892078   
 
 BIOME_NUM      11.0       12.0        13.0        14.0  
 2001 Set   0.028093  30.625437  147.242173    5.385856  
 2002 Set   0.579421  21.309403  127.365858   57.979280  
 2003 Set   3.122352  22.217026   38.661227   30.182481  
 2004 Set   0.398822  32.137129   34.109964   22.736264  
 2005 Set   0.349158  13.990746   22.521240   16.519034  
 2006 Set   0.253340  25.428614   77.414878   50.027010  
 2007 Set   2.121534  31.882145  144.079274    8.892084  
 2008 Set   0.850319  27.198326   78.636879   11.856112  
 2009 Set   0.140967  38.876000   16.841202   55.882772  
 2010 Set   0.054180  17.964252   54.040929   12.470606  
 2011 Set   0.025585  28.612882  392.616192   36.038243  
 2012 Set   0.416380  32.571210  134.570485   29.170373  
 2013 Set   0.272403  20.058766   22.884907   12.542899  
 2014 Set   0.220230  25.061315   36.647194   39.616765  
 2015 Set   0.428420  37.601079   25.200723  126.260365  
 2016 Set   0.367719  30.786320    9.806956   11.386205  
 2017 Set   0.137456  18.040140  134.035231    8.133004  ,
 BIOME_NUM      0.0        1.0         2.0       3.0        4.0        5.0   \
 2001 Out   0.001387  33.984645   63.303647  0.249090  27.503074   2.988824   
 2002 Out   0.000925  92.179191  123.950790  0.182666  18.431815   3.498946   
 2003 Out   0.002466  87.248021   94.628375  0.000000  15.073780   3.750636   
 2004 Out   0.001695  76.937281   63.581831  0.116242  24.932642   2.422520   
 2005 Out   0.002466  70.848358  119.429538  0.464969  46.940500   2.222516   
 2006 Out   0.002620  97.066869   63.749964  0.149454  14.935362   2.831517   
 2007 Out   0.001079  93.341844  134.423955  0.016606  16.076623  14.937376   
 2008 Out   0.000000  65.881658   76.124561  0.132848  19.790393   3.303437   
 2009 Out   0.000617  70.221701   53.032242  0.016606  16.844846   2.564096   
 2010 Out   0.002929  87.004219  124.308455  0.298908  26.783298   2.777583   
 2011 Out   0.000617  54.040112   41.571677  0.282302  39.491505  11.624951   
 2012 Out   0.003237  76.394546  104.805014  0.016606  15.914673   3.986596   
 2013 Out   0.001387  42.950186   62.710595  0.016606  19.587609   1.950601   
 2014 Out   0.003083  86.824124   71.872322  0.747271  45.635213   3.018038   
 2015 Out   0.001079  98.924786  122.880852  0.066424  37.222133   3.355123   
 2016 Out   0.000308  61.889555  118.069188  0.315514  18.074003   3.098938   
 2017 Out   0.012485  63.255581   88.040614  0.365332  17.111302   3.964124   
 
 BIOME_NUM      6.0          7.0         8.0         9.0        10.0      11.0  \
 2001 Out   0.386861   815.557296   13.036188  485.267852   7.249867  0.011538   
 2002 Out   0.925138   741.187762   74.974558  524.568080  16.578396  0.000000   
 2003 Out   0.831593   760.323669  173.502369  314.638573  30.486347  0.142974   
 2004 Out   0.181936  1104.017267   58.715830  420.152583  17.032308  0.006522   
 2005 Out   3.848794   675.623718  129.482394  690.765430  19.991221  0.001505   
 2006 Out   0.407076   630.683761   29.124694  266.332437  12.764686  0.003512   
 2007 Out   2.075416   787.905408   54.210781  331.447707   7.572272  0.002508   
 2008 Out   1.806674   738.215297   43.808032  511.007134  15.184844  0.001003   
 2009 Out   0.068573   631.543933   35.360335  380.965994  12.334106  0.000000   
 2010 Out   0.286182   637.623040   35.907717  411.459183  28.927350  0.000000   
 2011 Out   2.225245   733.761331   31.554533  344.061659  19.815171  0.001003   
 2012 Out   0.227519   672.721959   24.015523  230.536085   7.205324  0.001003   
 2013 Out   0.265174   503.359359   23.729316  288.700422  21.229934  0.001003   
 2014 Out   0.998070   738.201935   46.927104  302.583346  23.230117  0.000000   
 2015 Out   0.590598   558.773372   42.068260  398.447493  20.385743  0.135951   
 2016 Out   0.199773   642.144371    7.717577  592.751609   7.661357  0.007023   
 2017 Out   0.120894   723.049544   23.725978  368.456211  28.800085  0.000000   
 
 BIOME_NUM       12.0        13.0        14.0  
 2001 Out   16.470772   85.292188    7.373923  
 2002 Out   30.959345  244.736296   73.305474  
 2003 Out   50.107452   48.849129   11.133178  
 2004 Out   15.651180  127.542267   58.666067  
 2005 Out   22.414335   35.092467   16.374448  
 2006 Out   13.693264   95.253451  104.030154  
 2007 Out   40.296627   74.290154    9.578871  
 2008 Out   14.418755   50.108904   13.952620  
 2009 Out   18.689743   34.821626   16.663621  
 2010 Out   21.066561   98.507569   15.217754  
 2011 Out   20.386603  376.927072    9.795751  
 2012 Out   10.688092  327.186255   18.145635  
 2013 Out   26.557830   47.790275   18.687835  
 2014 Out   22.948588  115.005999   39.580618  
 2015 Out   28.330578   99.578076   85.089292  
 2016 Out   20.829790   20.329995   16.410594  
 2017 Out   66.311098  105.064426    7.554657  ,
 BIOME_NUM      0.0        1.0        2.0       3.0        4.0       5.0   \
 2001 Nov   0.001695  32.765022  30.459607  0.730665   5.721531  0.242701   
 2002 Nov   0.000000  42.109743  54.890877  0.780483  12.106087  3.285459   
 2003 Nov   0.000000  45.118676  62.031949  0.116242   4.203079  1.973073   
 2004 Nov   0.000000  56.242293  83.302317  0.963149   4.391329  2.510162   
 2005 Nov   0.000000  73.544268  83.990134  0.415151   5.777590  2.312406   
 2006 Nov   0.000308  46.163103  38.636990  0.348726  10.610474  0.573045   
 2007 Nov   0.000771  40.735141  77.689728  0.083030  12.010578  1.471939   
 2008 Nov   0.000000  40.598538  55.401390  1.643996   8.755666  1.759586   
 2009 Nov   0.000000  56.155921  48.694407  1.295270   6.261363  1.793294   
 2010 Nov   0.001233  39.582288  39.911745  2.275025  15.320165  1.426995   
 2011 Nov   0.001079  40.401904  43.405857  0.265696  12.699210  1.561829   
 2012 Nov   0.001695  36.463094  43.989737  0.199272   5.424623  1.197777   
 2013 Nov   0.000000  32.659048  41.565563  0.863513   7.188768  2.555107   
 2014 Nov   0.000000  33.488465  46.829658  0.498181  22.663270  0.878669   
 2015 Nov   0.001541  68.564705  84.552616  0.464969  13.839779  0.793274   
 2016 Nov   0.000154  36.569680  65.409896  1.378300   7.731368  0.961817   
 2017 Nov   0.000154  34.774244  41.522766  0.298908   8.023431  3.116916   
 
 BIOME_NUM      6.0          7.0        8.0          9.0       10.0  11.0  \
 2001 Nov   0.000396   800.815227   2.544155   123.222787  0.434822   0.0   
 2002 Nov   0.116930   684.890193  18.111982   972.666440  6.429007   0.0   
 2003 Nov   0.000000   769.524448  14.947016   299.770776  4.019455   0.0   
 2004 Nov   0.088391   981.341135   3.433650   617.970044  2.791347   0.0   
 2005 Nov   0.099490  1107.032602   8.068869   403.646487  5.953884   0.0   
 2006 Nov   0.002775  1009.874352   3.816650   386.373327  1.387189   0.0   
 2007 Nov   0.091959   805.529750   4.673602   350.273936  2.861343   0.0   
 2008 Nov   0.000000   888.503232   9.998889   347.859103  2.131690   0.0   
 2009 Nov   0.156568   825.063173   6.930716  1548.362697  1.295983   0.0   
 2010 Nov   0.000000   618.229916   5.807583   211.757205  6.422644   0.0   
 2011 Nov   0.004756   671.572836   7.554864   484.699656  5.035455   0.0   
 2012 Nov   0.000000   734.521846   6.865631   303.776558  3.033151   0.0   
 2013 Nov   0.000000   583.802989   7.564043   344.904484  5.300590   0.0   
 2014 Nov   0.000000   598.366900  12.508832   283.349909  1.667172   0.0   
 2015 Nov   0.000000   727.556957   9.015938  1035.272177  8.745231   0.0   
 2016 Nov   0.177972   744.282711   5.754180   735.075254  3.260107   0.0   
 2017 Nov   0.002775   641.145570   6.818069   280.262711  8.420705   0.0   
 
 BIOME_NUM       12.0        13.0       14.0  
 2001 Nov    5.075402  103.090578   2.927881  
 2002 Nov   63.445560  226.732565  11.458499  
 2003 Nov   13.693264   23.873438   9.181258  
 2004 Nov   10.023312   86.982738  20.314436  
 2005 Nov   26.433373   26.898907   7.988417  
 2006 Nov   28.974110  195.605475  23.748371  
 2007 Nov   21.461179   47.199969  13.121246  
 2008 Nov    7.151703   35.859584  11.711525  
 2009 Nov   21.324580   44.666757   9.759604  
 2010 Nov    9.983850   40.265381   4.735216  
 2011 Nov   12.849388   77.105057   5.855763  
 2012 Nov   11.662497  151.177011  11.892259  
 2013 Nov   12.217998   52.933452   5.205122  
 2014 Nov    6.201583   93.218121   9.181258  
 2015 Nov   57.073988   91.097199  14.747847  
 2016 Nov   25.817161   46.270509  13.771886  
 2017 Nov   36.271518  130.560101   7.988417  ,
 BIOME_NUM      0.0        1.0         2.0       3.0        4.0       5.0   \
 2001 Dez   0.000000  27.441196   17.546983  0.232484  12.718589  0.168543   
 2002 Dez   0.001233  49.350906   36.506285  0.448363   5.864102  0.294388   
 2003 Dez   0.000000  63.737671   72.798582  1.594178   2.405022  0.525853   
 2004 Dez   0.000000  56.472618  202.523981  3.487264   1.293521  0.660687   
 2005 Dez   0.001079  28.440907   25.216911  1.976116   2.447932  1.379803   
 2006 Dez   0.000000  59.290430   41.220126  0.464969  22.976788  0.869680   
 2007 Dez   0.000925  46.620079   36.151677  1.660602   1.305979  0.361805   
 2008 Dez   0.000771  38.321012   33.427921  0.315514   1.549596  0.083148   
 2009 Dez   0.000000  50.198700   36.604108  0.315514   3.565662  0.188768   
 2010 Dez   0.000000  54.009483   29.047289  1.843268   3.798205  0.404502   
 2011 Dez   0.000000  63.150832   31.795501  3.570294   2.027140  0.979795   
 2012 Dez   0.000925  43.851885   85.674522  1.942904   3.400252  0.301130   
 2013 Dez   0.000000  37.214714   26.277678  0.581211   3.786440  1.977567   
 2014 Dez   0.000000  33.865806   40.862461  1.594178   1.921250  0.328096   
 2015 Dez   0.000000  63.341340  144.420233  1.345088   1.745458  1.155079   
 2016 Dez   0.000000  32.838530   21.404874  3.038902   3.268754  1.341600   
 2017 Dez   0.000000   0.000000    0.000000  0.000000   0.000000  0.000000   
 
 BIOME_NUM      6.0          7.0        8.0          9.0        10.0  11.0  \
 2001 Dez   0.000000  1148.667716  18.591775   493.478286   3.586754   0.0   
 2002 Dez   0.000000  1334.297312   6.571080  2094.616927   4.397008   0.0   
 2003 Dez   0.001585  1379.184378  13.287349  1189.565819   7.075938   0.0   
 2004 Dez   0.000000  1051.410922   9.673464  1363.433816   5.069392   0.0   
 2005 Dez   0.001585  1240.612042   5.959448  1131.022684   5.147872   0.0   
 2006 Dez   0.012288  1278.806468   2.261286  1009.419256  17.602880   0.0   
 2007 Dez   0.000000  1161.896551   2.668484  1172.624106   4.078845   0.0   
 2008 Dez   0.000000  1055.165616   2.281312  1175.985933   4.920916   0.0   
 2009 Dez   0.000000  1115.934409   0.766000  1142.916922   1.158112   0.0   
 2010 Dez   0.000000  1062.367121   3.326844   744.942926   2.801953   0.0   
 2011 Dez   0.000000  1131.710912   7.888633  1153.684237   7.235019   0.0   
 2012 Dez   0.004756  1018.605795   2.444024  1107.186859   3.991881   0.0   
 2013 Dez   0.000000   892.228974   5.115846   914.284294   2.473184   0.0   
 2014 Dez   0.000000   923.814272   6.963258   778.135046   3.567664   0.0   
 2015 Dez   0.000000   806.393262   3.884238  1914.820751   5.898736   0.0   
 2016 Dez   0.000000  1010.840306  42.863465  1370.735136   4.015213   0.0   
 2017 Dez   0.000000     0.000000   0.000000     0.000000   0.000000   0.0   
 
 BIOME_NUM       12.0       13.0       14.0  
 2001 Dez    4.198134  95.979580   3.036321  
 2002 Dez   72.807126  60.272696  15.868394  
 2003 Dez   12.639936  11.206974  28.953493  
 2004 Dez   71.608093  42.905483  24.688185  
 2005 Dez   25.841445   7.581956  10.916298  
 2006 Dez   37.106288  35.691614  30.941561  
 2007 Dez   16.874497  44.220310   9.578871  
 2008 Dez    9.804754   4.761829  14.603260  
 2009 Dez   16.807716  10.875856  11.675379  
 2010 Dez    3.241943   6.160239  12.398312  
 2011 Dez    5.852497  48.275299  16.627474  
 2012 Dez    6.389785  20.822251  18.615542  
 2013 Dez    9.404064  11.012884  27.001572  
 2014 Dez    3.584958  17.314974  15.434634  
 2015 Dez    7.066708  18.853225  32.387428  
 2016 Dez   41.022118  12.427770  22.916997  
 2017 Dez    0.000000   0.000000   0.000000  ]

%%HTML
<h1 id="varintermonbio">Variação inter-anual por mês em cada bioma</h1>

«««

labels = years[::2]

for b in range(15):
    fig, ax = plt.subplots(4,3, figsize =(20,12) , sharey= True)
    fig.suptitle(biome_names_dict[b],fontsize=15)
    for i in range(4):
        for j in range(3):
            data = intra_biome[i*3 + j][b]
            slope, intercept, r_value, p_value, std_err = stats.linregress(years_float, data)
            ax[i][j].plot(years, intercept + slope*years_float, 'r', label=r"$R^2$: {:5.3f} ".format(r_value**2))
            ax[i][j].plot(years, data, 'o', label='_nolegend_')
            ax[i][j].set_xticks(labels) 
            ax[i][j].set_xticklabels(labels,fontsize=15)
            labels_y = ax[i][j].get_yticklabels()
            plt.setp(labels_y,fontsize=15)
            ax[i][j].set_title(calendar.month_name[i*3 + j +1],fontsize=15)
            ax[i][j].legend(fontsize=15)
    fig.tight_layout(rect=[0, 0.03, 1, 0.95])
    plt.show()

calendar.month_name[3]

'March'

sns.set_context("paper")

labels = years[::2]

for b in range(15):
    fig, ax = plt.subplots(4,3, figsize =(20,12) , sharey= True)
    fig.suptitle(biome_names_dict[b],fontsize=15)
    for i in range(4):
        for j in range(3):
            data = intra_biome[i*3 + j][b]
            slope, intercept, r_value, p_value, std_err = stats.linregress(years_float, data)
            ax[i][j].plot(years, intercept + slope*years_float, 'r', label=r"$R^2$: {:5.3f} ".format(r_value**2))
            ax[i][j].plot(years, data, 'o', label='_nolegend_')
            ax[i][j].set_xticks(labels) 
            ax[i][j].set_xticklabels(labels,fontsize=15)
            labels_y = ax[i][j].get_yticklabels()
            plt.setp(labels_y,fontsize=15)
            ax[i][j].set_title(calendar.month_name[i*3 + j +1],fontsize=15)
            ax[i][j].legend(fontsize=15)
    fig.tight_layout(rect=[0, 0.03, 1, 0.95])
    plt.show()

sns.set_style("whitegrid")
for b in range(15):
    fig, ax = plt.subplots(4,3, figsize =(20,12) , sharey= True)
    fig.suptitle(biome_names_dict[b],fontsize=15)
    for i in range(4):
        for j in range(3):
            data = intra_biome[i*3 + j][b]
            slope, intercept, r_value, p_value, std_err = stats.linregress(years_float, data)
            ax[i][j].plot(years, intercept + slope*years_float, 'r', label=r"$R^2$: {:5.3f} ".format(r_value**2))
            ax[i][j].plot(years, data, 'o', label='_nolegend_')
            ax[i][j].set_xticks(labels) 
            ax[i][j].set_xticklabels(labels,fontsize=15)
            labels_y = ax[i][j].get_yticklabels()
            plt.setp(labels_y,fontsize=15)
            ax[i][j].set_title(calendar.month_name[i*3 + j +1],fontsize=15)
            ax[i][j].legend(fontsize=15)
    fig.tight_layout(rect=[0, 0.03, 1, 0.95])
    plt.show()

labels = years[::2]
for b in range(15):
    fig, ax = plt.subplots(4,3, figsize =(20,12) , sharey= True)
    fig.suptitle(biome_names_dict[b],fontsize=15)
    for i in range(4):
        for j in range(3):
            data = intra_biome[i*3 + j][b]
            #slope, intercept, r_value, p_value, std_err = stats.linregress(years_float, data)
            #ax[i][j].plot(years, intercept + slope*years_float, 'r', label=r"$R^2$: {:5.3f} ".format(r_value**2))
            sns.regplot(years_float, data, ax=ax[i][j])
            #ax[i][j].set_xticks(labels) 
            #ax[i][j].set_xticklabels(labels,fontsize=15)
            #labels_y = ax[i][j].get_yticklabels()
            #plt.setp(labels_y,fontsize=15)
            ax[i][j].set_title(calendar.month_name[i*3 + j +1],fontsize=15)
            ax[i][j].set_ylabel('')
            #ax[i][j].legend(fontsize=15)
    #fig.tight_layout(rect=[0, 0.03, 1, 0.95])
    plt.show()

x = np.linspace(1,200,10)
y = 4*x
df = pd.DataFrame(y, index=x)

df

df.columns = ['y']

df.index.name= 'x'

df

df =pd.DataFrame(dict(zip(['x','y'],[x,y])))

df

ax= sns.regplot( x = 'x', y='y',data=df);ax.set(ylabel=''); plt.show()

Lost Pixels¶

MODIS Tile v=5, h=17

South Iberia + North Morocco

2001 June

hdf file: MCD64A1.A2001152.h17v05.006.2017012103748.hdf

«««

pwd

'C:\\Users\\alpha\\Documents\\MODIS\\MCD64A1'

cd ../MODIS/MCD64A1

C:\Users\alpha\Documents\MODIS\MCD64A1

import datetime


from pyhdf.SD import SD, SDC
#import matplotlib.path as mplPath
import subprocess
import re


DATADIR = 'C:/Users/alpha/Documents/dados/MCD64A1/'
NECOS = 847




def get_tilehv(fn):
    pattern = 'MCD64A1\.A\d{4}\d{3}\.h(\d{2})v(\d{2}).{22}'
    # MCD64A1.A2005032.h14v04.006.2017017093958.hdf
    matches= re.findall(pattern, fn)
    if matches:
        h, v = matches[0]
        return int(h), int(v)

def get_coord(h,v):
    lon_f = 'lon_coordh{}v{}.npy'.format(h,v)
    lat_f = 'lat_coordh{}v{}.npy'.format(h,v)
    lon = np.load(lon_f)
    lat = np.load(lat_f)
    return lon, lat

def get_julian(yr, mo):
    '''
    Return the first julian day of given month and year.
    '''
    dt = datetime.datetime(yr, mo, 1)
    tt = dt.timetuple()
    return tt.tm_yday

def julian2month(yr,jul):
    # month from 1 to 12
    values = [get_julian(yr,i) for i in range(1,13)]
    #return month from 0 to 11
    idx = [i for i in range(12) if values[i] == jul]
    return idx[0]



def get_month(fn):
    pattern = 'MCD64A1\.A(\d{4})(\d{3}).{29}'
    matches= re.findall(pattern, fn)
    year, julian = matches[0]
    year, julian = int(year), int(julian)
    return julian2month(year,julian)


file_ = DATADIR + 'MCD64A1.A2001152.h17v05.006.2017012103748.hdf'

h , v = get_tilehv(file_)
print('h: {}; v: {}'.format(h,v))
lon, lat = get_coord(h,v)

fich = SD(file_, SDC.READ)
# select sds
sds_obj = fich.select('Burn Date')
# get sds data
data = sds_obj.get()
attrs = sds_obj.attributes(full=1)
vra=attrs["valid_range"]
valid_range = vra[0]
fva=attrs["_FillValue"]
_FillValue = fva[0]
lat = lat.reshape(data.shape)
lon = lon.reshape(data.shape)

# Mask the data. logical_or from numpy
data = data.astype('double')
#invalid = logical_or(data < valid_range[0], data > valid_range[1])
invalid = np.logical_or(data < 1, data > valid_range[1])
invalid = np.logical_or(invalid, data == _FillValue)
# nan from numpy
data[invalid] = np.nan
# ma, isnan from numpy
data = np.ma.masked_array(data, np.isnan(data))

idx =np.logical_not(data.mask) 
lat_val =lat[idx]
lon_val = lon[idx]
data_val = data[idx] 

result = data_val.size
print('Result: {}'.format(result))

h: 17; v: 5
Result: 361

import pickle

from mpl_toolkits.basemap import Basemap

import matplotlib.path as mplPath

 m = Basemap(projection='cyl')
shpf = m.readshapefile('ecoregions2017/ecoregions2017','ecos')
rings =[mplPath.Path(eco,closed=True) for eco in m.ecos]
ecos_info =m.ecos_info

result = np.zeros((NECOS),dtype=int)

br_vh = []
with open('bounded_rings.txt', 'rb') as f:
    br_string = f.read()
    br = pickle.loads(br_string)
    br_vh = br[v,h]

not_included = []
for i, px in enumerate(data_val):
            pt = ( lon_val[i], lat_val[i])
            included = False 
            for ring_idx in br_vh:
                ring = rings[ring_idx]
                if ring.contains_point(pt):
                    ecoid = int(ecos_info[ring_idx]['ECO_ID'])
                    result[ecoid] += 1
                    included = True
                    break
            if not included:
                not_included.append(pt)

result.sum()

361

len(not_included)

0

Mapas¶

«««

Total por Ecoregiões¶

total_ecoreg = df_finall.iloc[:12*17].sum() / df_finall.iloc[12*17]

total_ecoreg

Eco_Id
0      0.745548
1       57379.8
2         72840
3       4467.17
4       6082.29
5       2419.18
6       13492.1
7       12388.9
8       12075.4
9       35355.6
10      2430.83
11      21054.4
12      1371.78
13            0
14      53693.8
15      6429.86
16      24463.8
17      14375.2
18       114995
19      93857.4
20      1280.95
21            0
22      7491.87
23      23056.3
24      26488.9
25      48026.2
26      11040.9
27      35.7731
28       133877
29      11898.1
         ...   
817     4812.59
818     9274.02
819         613
820       110.9
821           0
822     10.3596
823     2934.95
824      15.616
825     9.89902
826     696.044
827     1126.85
828     28606.5
829     4311.29
830     686.555
831     825.871
832           0
833     3.43063
834     484.523
835     10.9507
836     1.90378
837     11.3111
838     163.431
839           0
840    0.912961
841     1319.52
842     1.27572
843     39.5389
844           0
845    0.364836
846           0
Length: 847, dtype: object

ecos = m.ecos

cd -

C:\Users\alpha\Documents\TestEco2017

fig, ax = plt.subplots(figsize=(12, 8), frameon= False)
for spine in plt.gca().spines.values():
    spine.set_visible(False)
cmap = plt.get_cmap('OrRd')


min_val = 0
max_val = 1

norm = cls.Normalize(min_val, max_val)
cmmapable = cm.ScalarMappable(norm, cmap)
cmmapable.set_array(range(min_val, max_val))

ecos_info, ecos = get_ecos()

total_trans = np.log1p(total_ecoreg.values.astype('float'))/np.log1p(total_ecoreg.max())

patches = []
for info, ec in zip(ecos_info,ecos):
    patches.append(Polygon(np.array(ec),True, color = cmap(norm(total_trans[info['ECO_ID']] ))))

ax.add_collection(PatchCollection(patches, match_original= True,  zorder=2))


title = 'Index Burned Area 2001-2017'

cbaxes = inset_axes(ax, width="80%", height="1%", loc=3)
cb = plt.colorbar(cmmapable, cax=cbaxes,orientation="horizontal") 

cb.set_label(title, fontsize=20, family='Times New Roman')
cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=15, family='Times New Roman')

plt.show()
fig.savefig('totalecologstd', dpi=1200, bbox_inches='tight')

#total_ecoreg.values.astype('float')

#np.log1p(total_ecoreg.values.astype('float'))

#total_ecoreg.values

pwd

'C:\\Users\\alpha\\Documents\\MODIS\\MCD64A1'

np.log1p(total_ecoreg.max())

13.000021174830106

total_ecoreg.max()

442421.76013651653

total_ecoreg.size

847

Total por biomas¶

«««

areas_biome

0.0     6487.848621
1.0     1632.472566
2.0      327.121754
3.0       60.219125
4.0     1444.893009
5.0      444.991141
6.0     2522.867989
7.0     1796.152160
8.0     1198.433201
9.0      105.597352
10.0     471.456944
11.0    1993.369285
12.0     329.432038
13.0    2488.539746
14.0      27.665056
Name: AREAS, dtype: float64

df_finall.iloc[:12*17].groupby(df_finall.loc['Atributos'].loc['BIOME_NUM'], axis=1).sum().sum()/areas_biome

BIOME_NUM
0.0          0.745548
1.0      13398.214739
2.0      20490.636632
3.0      10292.195324
4.0       4456.835183
5.0       2389.240821
6.0       1974.625316
7.0     132496.632098
8.0       9704.680237
9.0     130837.334430
10.0      3543.434924
11.0       219.248387
12.0      4926.673229
13.0      8993.184069
14.0     10938.998465
dtype: float64

total_biome_area

BIOME_NUM
0.0          0.745548
1.0      13398.214739
2.0      20490.636632
3.0      10292.195324
4.0       4456.835183
5.0       2389.240821
6.0       1974.625316
7.0     132496.632098
8.0       9704.680237
9.0     130837.334430
10.0      3543.434924
11.0       219.248387
12.0      4926.673229
13.0      8993.184069
14.0     10938.998465
dtype: float64

fig, ax = plt.subplots(figsize=(12, 8), frameon= False)
for spine in plt.gca().spines.values():
    spine.set_visible(False)
cmap = plt.get_cmap('OrRd')


min_val = 0
max_val = int(total_biome_area.max()) +1

norm = cls.Normalize(min_val, max_val)
cmmapable = cm.ScalarMappable(norm, cmap)
cmmapable.set_array(range(min_val, max_val))

ecos_info, ecos = get_ecos()

#total_trans = np.log(total_pa + 1)/np.log(total_pa.max() + 1)

patches = []
for info, ec in zip(ecos_info,ecos):
    patches.append(Polygon(np.array(ec),True, color = cmap(norm(total_biome_area[info['BIOME_NUM']] )))) if info['BIOME_NAME'] \
    != 'N/A' else patches.append(Polygon(np.array(ec),True, color = cmap(norm(total_biome_area[0] ))))

ax.add_collection(PatchCollection(patches, match_original= True,  zorder=2))


title = 'Biomes Burned Px / Area 2001-2017'

cbaxes = inset_axes(ax, width="80%", height="1%", loc=3)
cb = plt.colorbar(cmmapable, cax=cbaxes,orientation="horizontal") 

cb.set_label(title, fontsize=20, family='Times New Roman')
cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=15, family='Times New Roman')
plt.show()
fig.savefig('biomesnotransform2', dpi=1200, bbox_inches='tight')

df_finall[:][:12*17].T

df_finall.T['Atributos']['BIOME_NUM']

Eco_Id
0       0
1       1
2       1
3       1
4       1
5       1
6       1
7       1
8       1
9       1
10      1
11      1
12      1
13      1
14      1
15      1
16      1
17      1
18      1
19      1
20      1
21      1
22      1
23      1
24      1
25      1
26      1
27      1
28      1
29      1
       ..
817    13
818    13
819    13
820    13
821    13
822    13
823    13
824    13
825    13
826    13
827    13
828    13
829    13
830    13
831    13
832    13
833    13
834    13
835    13
836    13
837    13
838    13
839    13
840    13
841    13
842    13
843    13
844    13
845    13
846    13
Name: BIOME_NUM, Length: 847, dtype: object

lala = pd.concat([df_finall[:][:12*17].T,df_finall.T['Atributos']['BIOME_NUM']], axis=1)

start = datetime.datetime.now()
fig, ax = plt.subplots(figsize=(12, 8), frameon= False)
for spine in plt.gca().spines.values():
    spine.set_visible(False)
cmap = plt.get_cmap('OrRd')


min_val = 0
max_val = 1

norm = cls.Normalize(min_val, max_val)
cmmapable = cm.ScalarMappable(norm, cmap)
cmmapable.set_array(range(min_val, max_val))

ecos_info, ecos = get_ecos()

total_trans = np.log1p(total_biome_area)/np.log1p(total_biome_area.max())

patches = []
for info, ec in zip(ecos_info,ecos):
    patches.append(Polygon(np.array(ec),True, color = cmap(norm(total_trans[info['BIOME_NUM']] )))) if info['BIOME_NAME'] \
    != 'N/A' else patches.append(Polygon(np.array(ec),True, color = cmap(norm(total_trans[0] )))) #cuidado estou a eliminar rock

ax.add_collection(PatchCollection(patches, match_original= True,  zorder=2))


title = 'Biomes Index Burned  2001-2017'

cbaxes = inset_axes(ax, width="80%", height="1%", loc=3)
cb = plt.colorbar(cmmapable, cax=cbaxes,orientation="horizontal") 

cb.set_label(title, fontsize=20, family='Times New Roman')
cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=15, family='Times New Roman')
plt.show()
fig.savefig('biomeslogtransform2', dpi=1200, bbox_inches='tight')
print('It took {} minutes.'.format((datetime.datetime.now()-start)/60))

It took 0:00:10.210871 minutes.

total_trans

BIOME_NUM
0.0     0.047232
1.0     0.805723
2.0     0.841742
3.0     0.783363
4.0     0.712412
5.0     0.659567
6.0     0.643415
7.0     1.000000
8.0     0.778380
9.0     0.998931
10.0    0.692972
11.0    0.457403
12.0    0.720908
13.0    0.771925
14.0    0.788530
dtype: float64

Variação interanual por ecorregiões¶

«««

import time

start = datetime.datetime.now()
time.sleep(29)
datetime.datetime.now()-start

datetime.timedelta(0, 29, 10693)

print(str(datetime.timedelta(0, 29, 10693)).format('%m:%s'))

0:00:29.010693

datetime.timedelta(0, 29, 10693).seconds

29

len([ df_finall[n*12: (n+1) *12].sum() for n in range(17)])

17

n= 1
df_finall[n*12: (n+1) *12].sum()

Eco_Id
0         221.0
1       52854.0
2       15594.0
3        3825.0
4           0.0
5         108.0
6        1301.0
7        2866.0
8        4505.0
9         641.0
10        778.0
11      22253.0
12        577.0
13          0.0
14      12427.0
15         16.0
16       1430.0
17       2847.0
18      79840.0
19      13759.0
20         16.0
21          0.0
22        430.0
23       5164.0
24      61463.0
25      18019.0
26      10136.0
27          0.0
28      84137.0
29       1697.0
         ...   
817     32739.0
818      1279.0
819       894.0
820       258.0
821         0.0
822         0.0
823       460.0
824        10.0
825         0.0
826      5482.0
827      3801.0
828    381008.0
829       210.0
830       389.0
831      3347.0
832         0.0
833        40.0
834         0.0
835         0.0
836         0.0
837        67.0
838         0.0
839         0.0
840         3.0
841      2556.0
842         3.0
843         4.0
844         0.0
845        15.0
846         0.0
Length: 847, dtype: float64

len(areas)

847

intereco =[ df_finall[n*12: (n+1) *12].sum()/areas for n in range(17)]

intereco_max= max(map(max,intereco))

for yr in range(17):
    fig, ax = plt.subplots(figsize=(12, 8), frameon= False)
    for spine in plt.gca().spines.values():
        spine.set_visible(False)
    cmap = plt.get_cmap('OrRd')


    min_val = 0
    max_val = 1

    norm = cls.Normalize(min_val, max_val)
    cmmapable = cm.ScalarMappable(norm, cmap)
    cmmapable.set_array(range(min_val, max_val))

    ecos_info, ecos = get_ecos()

    total_trans = np.log1p(intereco[yr])/np.log1p(intereco_max)

    patches = []
    for info, ec in zip(ecos_info,ecos):
        patches.append(Polygon(np.array(ec),True, color = cmap(norm(total_trans[info['ECO_ID']] ))))

    ax.add_collection(PatchCollection(patches, match_original= True,  zorder=2))


    title = 'Index Burned Area ' + years[yr]

    cbaxes = inset_axes(ax, width="80%", height="1%", loc=3)
    cb = plt.colorbar(cmmapable, cax=cbaxes,orientation="horizontal") 

    cb.set_label(title, fontsize=20, family='Times New Roman')
    cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=15, family='Times New Roman')

    plt.show()
    fig.savefig('ecologstd' + years[yr], dpi=1200, bbox_inches='tight')

Variação inter-anual por biomas¶

«««

annual_biome

[0.0        0.070748
 1.0      476.112748
 2.0      946.678098
 3.0      382.337005
 4.0      193.003218
 5.0       49.000975
 6.0       60.003140
 7.0     8077.196531
 8.0      708.472528
 9.0     6661.132970
 10.0     210.577024
 11.0       7.037331
 12.0     264.166778
 13.0     881.544288
 14.0     280.317529
 dtype: float64, 0.0         0.034064
 1.0       674.108112
 2.0      1075.498635
 3.0       475.613019
 4.0       259.586694
 5.0       222.334763
 6.0       147.100443
 7.0      7791.927270
 8.0       896.827624
 9.0     10757.476237
 10.0      269.159255
 11.0       11.232239
 12.0      338.886287
 13.0     1126.610899
 14.0      501.499084
 dtype: float64, 0.0        0.027436
 1.0      876.820248
 2.0     1272.244951
 3.0      772.644899
 4.0      252.820795
 5.0      217.116682
 6.0      226.887020
 7.0     8372.760020
 8.0      679.262724
 9.0     6664.305398
 10.0     303.836865
 11.0      38.992775
 12.0     370.555945
 13.0     225.426578
 14.0     792.552177
 dtype: float64, 0.0        0.023120
 1.0      957.398018
 2.0     1653.130656
 3.0      312.674751
 4.0      232.042786
 5.0       46.358226
 6.0       88.036711
 7.0     8755.643506
 8.0      559.566440
 9.0     8355.370538
 10.0     193.387755
 11.0      41.666138
 12.0     337.049793
 13.0     483.547430
 14.0     773.972782
 dtype: float64, 0.0        0.033293
 1.0      969.626708
 2.0     1297.529114
 3.0      977.911912
 4.0      259.779096
 5.0       67.183360
 6.0       89.737553
 7.0     8854.214220
 8.0      675.703910
 9.0     8294.052712
 10.0     238.218996
 11.0      11.775540
 12.0     324.088091
 13.0     310.801546
 14.0     680.063699
 dtype: float64, 0.0        0.058879
 1.0      766.412267
 2.0     1118.088281
 3.0      568.988670
 4.0      353.606805
 5.0      102.089673
 6.0       94.437363
 7.0     7941.017089
 8.0      710.640359
 9.0     7490.756050
 10.0     230.317957
 11.0       4.434703
 12.0     244.390317
 13.0     616.475587
 14.0     869.942226
 dtype: float64, 0.0        0.101420
 1.0     1081.095656
 2.0     1553.819009
 3.0      495.689697
 4.0      255.561483
 5.0      154.728923
 6.0       59.190176
 7.0     8751.938363
 8.0      585.682205
 9.0     8367.908731
 10.0     212.961123
 11.0       8.980774
 12.0     310.658917
 13.0     489.046639
 14.0     624.180927
 dtype: float64, 0.0        0.028823
 1.0      691.240406
 2.0     1172.083468
 3.0      676.462831
 4.0      406.588582
 5.0      234.986700
 6.0      120.913183
 7.0     7970.049707
 8.0      813.971942
 9.0     8282.878189
 10.0     209.747680
 11.0       4.449753
 12.0     197.324463
 13.0     341.816923
 14.0     508.186220
 dtype: float64, 0.0        0.024816
 1.0      813.706783
 2.0     1088.383745
 3.0      614.937527
 4.0      353.201238
 5.0       81.006107
 6.0       71.038596
 7.0     7353.475552
 8.0      397.453108
 9.0     9028.332491
 10.0     186.277880
 11.0       6.773958
 12.0     317.106377
 13.0     201.442633
 14.0     740.103340
 dtype: float64, 0.0        0.021887
 1.0     1037.210079
 2.0     1411.281867
 3.0      335.109484
 4.0      281.539185
 5.0       79.864511
 6.0       83.170820
 7.0     7817.391705
 8.0      546.414268
 9.0     6298.339781
 10.0     230.595819
 11.0      28.556676
 12.0     276.767253
 13.0     390.489644
 14.0     561.430285
 dtype: float64, 0.0        0.028207
 1.0      651.342645
 2.0     1180.759136
 3.0     1441.020598
 4.0      267.334673
 5.0      138.863439
 6.0      117.194400
 7.0     8129.748315
 8.0      414.446128
 9.0     7868.265518
 10.0     234.267416
 11.0       2.951786
 12.0     187.046167
 13.0    1211.462668
 14.0     630.434303
 dtype: float64, 0.0        0.088627
 1.0      777.016427
 2.0     1293.328843
 3.0      761.269775
 4.0      282.519880
 5.0      218.647049
 6.0      176.553431
 7.0     8195.876899
 8.0      452.575079
 9.0     7810.309519
 10.0     190.375815
 11.0       6.328983
 12.0     284.079838
 13.0     891.924271
 14.0     547.441518
 dtype: float64, 0.0        0.020654
 1.0      661.425510
 2.0      983.462567
 3.0      628.072889
 4.0      193.003218
 5.0      148.020924
 6.0      141.622947
 7.0     7105.716476
 8.0      171.562336
 9.0     6680.934603
 10.0     178.822268
 11.0       5.474149
 12.0     183.200154
 13.0     237.551761
 14.0     617.529937
 dtype: float64, 0.0        0.021425
 1.0      711.453916
 2.0      920.990414
 3.0      297.181334
 4.0      296.385959
 5.0      133.816147
 6.0      171.932500
 7.0     7115.781325
 8.0      588.948971
 9.0     6600.411750
 10.0     186.477262
 11.0       2.043274
 12.0     264.922625
 13.0     371.917709
 14.0     756.622374
 dtype: float64, 0.0        0.029594
 1.0      857.402464
 2.0     1294.707535
 3.0      220.909884
 4.0      214.864352
 5.0      174.392236
 6.0      131.431768
 7.0     7035.254183
 8.0      555.915840
 9.0     8906.501784
 10.0     176.915413
 11.0      11.997275
 12.0     336.661245
 13.0     398.958064
 14.0     934.283322
 dtype: float64, 0.0        0.020192
 1.0      760.193479
 2.0     1099.969645
 3.0      549.725686
 4.0      157.682263
 5.0       91.053498
 6.0       98.471660
 7.0     7295.150872
 8.0      350.450071
 9.0     7234.452273
 10.0     122.316578
 11.0      18.823908
 12.0     262.661157
 13.0     172.461782
 14.0     660.146877
 dtype: float64, 0.0        0.112364
 1.0      635.649273
 2.0     1128.680668
 3.0      781.645362
 4.0      197.314956
 5.0      229.777608
 6.0       96.903604
 7.0     5933.490065
 8.0      596.786704
 9.0     5535.905886
 10.0     169.179818
 11.0       7.729125
 12.0     427.107822
 13.0     641.705644
 14.0     460.291865
 dtype: float64]

type(annual_biome[0])

pandas.core.series.Series

denominador = np.log1p(max(map(max,annual_biome)))
for yr in range(17):
    fig, ax = plt.subplots(figsize=(12, 8), frameon= False)
    for spine in plt.gca().spines.values():
        spine.set_visible(False)
    cmap = plt.get_cmap('OrRd')


    min_val = 0
    max_val = 1

    norm = cls.Normalize(min_val, max_val)
    cmmapable = cm.ScalarMappable(norm, cmap)
    cmmapable.set_array(range(min_val, max_val))

    ecos_info, ecos = get_ecos()
    

    total_trans = np.log1p(annual_biome[yr])/denominador

    patches = []
    for info, ec in zip(ecos_info,ecos):
        patches.append(Polygon(np.array(ec),True, color = cmap(norm(total_trans.loc[info['BIOME_NUM']] )))) if info['BIOME_NAME'] \
        != 'N/A' else patches.append(Polygon(np.array(ec),True, color = cmap(norm(total_trans.iloc[0] )))) #cuidado estou a eliminar rock

    ax.add_collection(PatchCollection(patches, match_original= True,  zorder=2))


    title = 'Biomes Index Burned' +  years[yr]

    cbaxes = inset_axes(ax, width="80%", height="1%", loc=3)
    cb = plt.colorbar(cmmapable, cax=cbaxes,orientation="horizontal") 

    cb.set_label(title, fontsize=20, family='Times New Roman')
    cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=15, family='Times New Roman')
    plt.show()
    fig.savefig('biomeslogtransform' + years[yr], dpi=1200, bbox_inches='tight')

annual_biome[0]

0.0        0.070748
1.0      476.112748
2.0      946.678098
3.0      382.337005
4.0      193.003218
5.0       49.000975
6.0       60.003140
7.0     8077.196531
8.0      708.472528
9.0     6661.132970
10.0     210.577024
11.0       7.037331
12.0     264.166778
13.0     881.544288
14.0     280.317529
dtype: float64

max(map(max,annual_biome))

10757.476236875702

open('df_finall','wb').write(pickle.dumps(df_finall))

463793

lala = pickle.loads(open('df_finall','rb').read())

pwd

'C:\\Users\\alpha\\Documents\\TestEco2017'

int(ecos_info[0]['BIOME_NUM'])

11

	BIOME_NUM	0.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	14.0
2001	Jan	36.0	44159.0	96226.0	148.0	1024.0	105.0	137.0	1467138.0	164076.0	139155.0	4084.0	0.0	28963.0	373792.0	191.0
	Fev	0.0	97045.0	42788.0	508.0	1073.0	93.0	0.0	569728.0	24759.0	53868.0	3789.0	0.0	3882.0	16204.0	1735.0
	Mar	0.0	73085.0	18887.0	2066.0	7166.0	722.0	9.0	261311.0	18602.0	33808.0	1445.0	0.0	4467.0	22553.0	2379.0
	Abr	0.0	38649.0	17042.0	8286.0	31171.0	3079.0	6461.0	238841.0	50578.0	30531.0	331.0	0.0	6842.0	105658.0	1789.0
	Mai	0.0	16240.0	19069.0	11707.0	2195.0	970.0	2777.0	648311.0	13510.0	10107.0	3190.0	0.0	874.0	106221.0	719.0
	Jun	80.0	75637.0	14232.0	203.0	10333.0	1375.0	11724.0	1305506.0	48301.0	42774.0	23325.0	412.0	4912.0	93165.0	66.0
	Jul	166.0	44735.0	7605.0	1.0	36274.0	1862.0	81164.0	952967.0	193020.0	83115.0	12382.0	7020.0	6910.0	105022.0	65.0
	Ago	139.0	134837.0	30782.0	0.0	73278.0	7595.0	43006.0	2227245.0	224408.0	130052.0	25637.0	6517.0	11605.0	297078.0	293.0
	Set	18.0	99090.0	26636.0	32.0	49972.0	4491.0	5125.0	1870394.0	70850.0	63623.0	19781.0	56.0	10089.0	366418.0	149.0
	Out	9.0	55479.0	20708.0	15.0	39739.0	1330.0	976.0	1464865.0	15623.0	51243.0	3418.0	23.0	5426.0	212253.0	204.0
	Nov	11.0	53488.0	9964.0	44.0	8267.0	108.0	1.0	1438386.0	3049.0	13012.0	205.0	0.0	1672.0	256545.0	81.0
	Dez	0.0	44797.0	5740.0	14.0	18377.0	75.0	0.0	2063182.0	22281.0	52110.0	1691.0	0.0	1383.0	238849.0	84.0
2002	Jan	0.0	63333.0	102876.0	148.0	14575.0	9.0	0.0	1387443.0	20526.0	181209.0	2822.0	0.0	7041.0	178296.0	835.0
	Fev	22.0	69626.0	36954.0	230.0	5172.0	1133.0	3.0	642262.0	26235.0	127152.0	2602.0	0.0	5189.0	54090.0	2015.0
	Mar	4.0	71184.0	23280.0	2561.0	50260.0	6214.0	2828.0	275331.0	20494.0	62467.0	1327.0	0.0	5046.0	35658.0	2132.0
	Abr	0.0	53521.0	16540.0	5928.0	20649.0	4871.0	8610.0	260205.0	27855.0	45086.0	1205.0	0.0	6045.0	114910.0	1202.0
	Mai	1.0	35405.0	15729.0	15619.0	24964.0	8348.0	17879.0	783391.0	26776.0	35162.0	12696.0	339.0	1811.0	86389.0	1236.0
	Jun	33.0	30952.0	7829.0	3206.0	25702.0	18365.0	48479.0	860988.0	36880.0	50802.0	10531.0	1779.0	5698.0	38406.0	192.0
	Jul	77.0	66461.0	13117.0	794.0	35885.0	16107.0	144705.0	1462381.0	133996.0	93266.0	32546.0	2674.0	10880.0	181547.0	448.0
	Ago	56.0	190316.0	34746.0	68.0	78047.0	15391.0	117389.0	1844769.0	266976.0	102957.0	24626.0	16443.0	7825.0	474105.0	1426.0
	Set	14.0	219878.0	30303.0	2.0	67224.0	25349.0	28593.0	1520663.0	395617.0	58570.0	25622.0	1155.0	7020.0	316955.0	1604.0
	Out	6.0	150480.0	40547.0	11.0	26632.0	1557.0	2334.0	1331286.0	89852.0	55393.0	7816.0	0.0	10199.0	609036.0	2028.0
	Nov	0.0	68743.0	17956.0	47.0	17492.0	1462.0	295.0	1230167.0	21706.0	102711.0	3031.0	0.0	20901.0	564233.0	317.0
	Dez	8.0	80564.0	11942.0	27.0	8473.0	131.0	0.0	2396601.0	7875.0	221186.0	2073.0	0.0	23985.0	149991.0	439.0
2003	Jan	19.0	114178.0	86283.0	117.0	40156.0	574.0	0.0	1793645.0	29681.0	77635.0	32753.0	0.0	12404.0	40966.0	1504.0
	Fev	0.0	81908.0	74998.0	499.0	11679.0	565.0	21.0	677328.0	25727.0	42313.0	8569.0	0.0	15338.0	11804.0	1827.0
	Mar	2.0	230596.0	42861.0	9444.0	65325.0	11145.0	80199.0	313488.0	39357.0	61973.0	2123.0	635.0	9290.0	22216.0	6368.0
	Abr	17.0	111599.0	20957.0	8502.0	57143.0	3350.0	30556.0	213280.0	89623.0	56628.0	1715.0	48.0	3039.0	31189.0	4768.0
	Mai	4.0	87695.0	32801.0	22392.0	60749.0	40606.0	166185.0	880076.0	139087.0	32432.0	10712.0	3555.0	2278.0	35339.0	3502.0
	Jun	10.0	48593.0	8712.0	5133.0	39583.0	6040.0	99789.0	928390.0	29528.0	45973.0	10717.0	8919.0	7405.0	18216.0	582.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2015	Jul	64.0	80281.0	9325.0	188.0	17009.0	13190.0	130222.0	1575435.0	66097.0	88169.0	12532.0	6723.0	9571.0	55333.0	545.0
	Ago	23.0	123802.0	26581.0	43.0	24924.0	36110.0	35852.0	1804702.0	165693.0	68178.0	14723.0	9958.0	8278.0	111268.0	1213.0
	Set	68.0	192105.0	50785.0	1.0	62937.0	8708.0	9493.0	1675209.0	76360.0	42268.0	10854.0	854.0	12387.0	62713.0	3493.0
	Out	7.0	161492.0	40197.0	4.0	53782.0	1493.0	1490.0	1003642.0	50416.0	42075.0	9611.0	271.0	9333.0	247804.0	2354.0
	Nov	10.0	111930.0	27659.0	28.0	19997.0	353.0	0.0	1306803.0	10805.0	109322.0	4123.0	0.0	18802.0	226699.0	408.0
	Dez	0.0	103403.0	47243.0	81.0	2522.0	514.0	0.0	1448405.0	4655.0	202200.0	2781.0	0.0	2328.0	46917.0	896.0
2016	Jan	0.0	136147.0	67805.0	228.0	6213.0	198.0	0.0	1783532.0	4527.0	110691.0	2738.0	0.0	10040.0	24001.0	1066.0
	Fev	0.0	153625.0	48328.0	1076.0	7454.0	662.0	0.0	522278.0	11056.0	39469.0	1196.0	0.0	4023.0	11610.0	2833.0
	Mar	0.0	141674.0	28771.0	1958.0	25344.0	3820.0	4106.0	184718.0	35657.0	16137.0	768.0	1.0	3694.0	16440.0	4200.0
	Abr	0.0	152920.0	35248.0	16118.0	35583.0	4542.0	8354.0	233863.0	67298.0	25746.0	482.0	13.0	2998.0	40496.0	3997.0
	Mai	0.0	57105.0	22210.0	9471.0	29933.0	8938.0	38526.0	486084.0	43027.0	35287.0	6872.0	728.0	1797.0	42846.0	2381.0
	Jun	14.0	36794.0	9093.0	3645.0	5245.0	5981.0	33566.0	1052086.0	16300.0	80140.0	7679.0	4410.0	7948.0	8858.0	718.0
	Jul	43.0	82850.0	16921.0	78.0	12706.0	3058.0	98713.0	1409196.0	59234.0	64928.0	8924.0	15578.0	6451.0	23904.0	399.0
	Ago	66.0	128670.0	36267.0	220.0	27514.0	7157.0	13858.0	1726692.0	74818.0	49384.0	13312.0	16046.0	10555.0	39953.0	885.0
	Set	5.0	136870.0	28159.0	25.0	35833.0	3758.0	50355.0	1398895.0	40560.0	57196.0	8654.0	733.0	10142.0	24405.0	315.0
	Out	2.0	101033.0	38623.0	19.0	26115.0	1379.0	504.0	1153389.0	9249.0	62593.0	3612.0	14.0	6862.0	50592.0	454.0
	Nov	1.0	59699.0	21397.0	83.0	11171.0	428.0	449.0	1336845.0	6896.0	77622.0	1537.0	0.0	8505.0	115146.0	381.0
	Dez	0.0	53608.0	7002.0	183.0	4723.0	597.0	0.0	1815623.0	51369.0	144746.0	1893.0	0.0	13514.0	30927.0	634.0
2017	Jan	3.0	46080.0	27916.0	1293.0	18841.0	428.0	0.0	906400.0	58233.0	104420.0	2665.0	0.0	9084.0	6077.0	804.0
	Fev	17.0	125103.0	106284.0	1502.0	8437.0	1469.0	182.0	491538.0	21649.0	55574.0	1039.0	0.0	4910.0	12986.0	2058.0
	Mar	1.0	153770.0	45978.0	4789.0	66277.0	4759.0	7678.0	200176.0	70534.0	57956.0	1046.0	98.0	18462.0	18882.0	3625.0
	Abr	2.0	98054.0	51499.0	15632.0	81289.0	441.0	23614.0	175365.0	80497.0	40101.0	791.0	6.0	15405.0	27826.0	3005.0
	Mai	0.0	27408.0	17844.0	12186.0	8940.0	1293.0	5824.0	581731.0	28499.0	13330.0	5167.0	14.0	9749.0	78949.0	1093.0
	Jun	169.0	27737.0	7922.0	11539.0	17519.0	5887.0	26505.0	1027866.0	41388.0	36480.0	9710.0	2493.0	12575.0	90870.0	496.0
	Jul	103.0	72117.0	9004.0	49.0	9459.0	25513.0	65054.0	1290441.0	121570.0	76018.0	12582.0	10798.0	16018.0	254144.0	762.0
	Ago	101.0	105367.0	17681.0	38.0	16369.0	39391.0	102445.0	1588769.0	135978.0	70023.0	17006.0	1724.0	14763.0	187263.0	236.0
	Set	251.0	222013.0	42705.0	2.0	21651.0	19917.0	12861.0	1944863.0	120256.0	62172.0	12207.0	274.0	5943.0	333552.0	225.0
	Out	81.0	103263.0	28800.0	22.0	24724.0	1764.0	305.0	1298707.0	28434.0	38908.0	13578.0	0.0	21845.0	261457.0	209.0
	Nov	1.0	56768.0	13583.0	18.0	11593.0	1387.0	7.0	1151595.0	8171.0	29595.0	3970.0	0.0	11949.0	324904.0	221.0
	Dez	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

	Ass.j	Roub.j	RD.j	RO.j	T.j
count	50.00000	50.000000	50.000000	50.000000	50.000000
mean	3.73000	1.176000	4.554000	1.382000	2.480000
std	2.55265	0.527435	1.863967	0.929711	2.251349
min	0.80000	0.400000	1.400000	0.100000	1.000000
25%	1.60000	0.800000	3.125000	0.700000	1.450000
50%	2.70000	1.100000	4.300000	1.100000	2.100000
75%	5.75000	1.500000	5.900000	1.800000	2.800000
max	11.10000	2.400000	9.100000	4.100000	17.100000

	0
1.000000	4.000000
23.111111	92.444444
45.222222	180.888889
67.333333	269.333333
89.444444	357.777778
111.555556	446.222222
133.666667	534.666667
155.777778	623.111111
177.888889	711.555556
200.000000	800.000000

	y
x
1.000000	4.000000
23.111111	92.444444
45.222222	180.888889
67.333333	269.333333
89.444444	357.777778
111.555556	446.222222
133.666667	534.666667
155.777778	623.111111
177.888889	711.555556
200.000000	800.000000

	x	y
0	1.000000	4.000000
1	23.111111	92.444444
2	45.222222	180.888889
3	67.333333	269.333333
4	89.444444	357.777778
5	111.555556	446.222222
6	133.666667	534.666667
7	155.777778	623.111111
8	177.888889	711.555556
9	200.000000	800.000000

	Eco_Id	0	1	2	3	4	5	6	7	8	9	...	837	838	839	840	841	842	843	844	845	846
2001	Jan	36	1656	1332	0	0	0	378	1678	457	0	...	0	0	0	0	107	0	0	0	0	0
	Fev	0	965	1335	0	0	0	91	185	299	0	...	0	0	0	0	13	0	0	0	0	0
	Mar	0	47	1294	24	0	0	0	30	232	0	...	0	0	0	0	26	0	0	0	0	0
	Abr	0	519	363	114	0	0	0	0	0	0	...	0	0	0	0	41	0	0	0	0	0
	Mai	0	3407	0	36	0	0	0	0	7	0	...	4	0	0	0	84	0	0	0	0	0
	Jun	80	28944	0	34	0	4	0	0	0	0	...	2	0	0	0	187	0	0	0	0	0
	Jul	166	4019	0	343	8	0	0	0	0	6	...	0	0	0	0	23	0	0	0	0	0
	Ago	139	5190	0	616	0	6	0	0	4	42	...	7	0	0	0	2	0	11	0	0	0
	Set	18	4374	0	181	5	0	0	0	1	103	...	6	0	0	0	0	0	0	0	0	0
	Out	9	707	195	0	0	0	0	0	81	674	...	0	0	0	0	55	3	0	0	0	0
	Nov	11	0	12343	0	0	0	10	6	11	1728	...	0	0	0	0	185	0	0	0	0	0
	Dez	0	118	2649	0	0	15	53	396	1118	40	...	0	0	0	0	56	0	0	0	0	0
2002	Jan	0	2640	1260	1	0	0	843	2051	1457	0	...	0	0	0	0	288	0	0	0	0	0
	Fev	22	2501	1641	12	0	0	0	63	1958	0	...	0	0	0	0	21	0	0	0	0	0
	Mar	4	2	1014	25	0	0	0	6	36	0	...	0	0	0	0	230	0	0	0	0	0
	Abr	0	330	38	60	0	0	0	0	0	0	...	17	0	0	0	539	0	0	0	0	0
	Mai	1	10298	0	676	0	11	0	0	3	0	...	45	0	0	0	148	0	0	0	0	0
	Jun	33	10677	0	405	0	32	0	0	0	0	...	1	0	0	1	366	0	1	0	0	0
	Jul	77	9630	0	758	0	42	0	0	15	0	...	0	0	0	2	71	0	1	0	0	0
	Ago	56	8721	0	1498	0	23	0	0	137	6	...	0	0	0	0	9	0	1	0	0	0
	Set	14	5632	0	216	0	0	0	0	607	83	...	4	0	0	0	37	0	1	0	0	0
	Out	6	2374	44	66	0	0	0	0	210	200	...	0	0	0	0	131	3	0	0	15	0
	Nov	0	1	4152	11	0	0	67	7	7	311	...	0	0	0	0	297	0	0	0	0	0
	Dez	8	48	7445	97	0	0	391	739	75	41	...	0	0	0	0	419	0	0	0	0	0
2003	Jan	19	1791	1966	59	0	0	1716	1467	936	24	...	0	0	0	0	122	0	252	0	0	0
	Fev	0	4915	1822	369	0	13	61	322	1620	49	...	0	0	0	0	38	0	0	0	0	0
	Mar	2	170	1897	405	0	24	37	165	527	14	...	0	0	0	0	174	0	0	0	0	0
	Abr	17	20	195	282	0	4	2	0	4	3	...	0	0	0	0	172	0	0	0	0	0
	Mai	4	9047	0	870	0	19	0	0	0	10	...	0	0	0	0	7	0	0	0	0	0
	Jun	10	6591	0	225	13	208	0	0	0	6	...	0	0	0	0	104	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2015	Nov	10	0	2147	1	0	2	225	69	0	116	...	0	0	0	0	163	0	1	0	0	0
2015	Dez	0	5	1153	25	0	2	117	452	5	0	...	0	0	0	0	17	0	0	0	0	0
2016	Jan	0	1824	2360	227	0	82	895	4987	483	16	...	0	0	0	0	639	0	0	0	0	0
	Fev	0	1747	1302	1141	0	76	20	298	2078	0	...	0	0	0	0	434	0	0	0	0	0
	Mar	0	116	38	450	0	0	0	0	554	0	...	0	0	0	6	287	0	0	0	0	0
	Abr	0	63	0	84	0	1	0	0	0	0	...	0	0	0	4	364	0	0	0	0	0
	Mai	0	4820	0	1486	20	54	0	0	4	0	...	0	0	0	0	216	0	0	0	0	0
	Jun	14	8039	0	1924	10	500	0	0	6	0	...	0	0	0	0	55	0	0	0	0	0
	Jul	43	6265	0	5993	0	1833	0	0	11	2	...	0	0	0	0	15	0	0	0	0	0
	Ago	66	8081	0	1222	8	364	0	0	5	66	...	0	0	0	2	2	0	0	0	0	0
	Set	5	5227	0	235	14	0	0	0	139	81	...	0	0	0	0	7	0	0	0	0	0
	Out	2	3551	14	86	0	0	0	0	189	325	...	0	0	0	0	262	0	0	0	0	0
	Nov	1	293	3039	28	0	0	5	0	309	536	...	0	0	0	0	413	0	0	0	0	0
	Dez	0	487	1027	56	0	1	904	578	1015	222	...	0	0	0	0	289	0	0	0	0	0
2017	Jan	3	2525	618	221	0	10	106	1056	797	8	...	0	0	0	0	648	0	0	0	0	0
	Fev	17	269	1182	212	0	42	1006	2778	69	1	...	0	0	0	0	210	0	0	0	0	0
	Mar	1	2	842	621	0	69	19	138	45	0	...	0	0	0	0	57	0	0	0	0	0
	Abr	2	293	76	383	0	23	0	0	5	0	...	0	0	0	0	221	1	18	0	0	0
	Mai	0	4934	0	886	0	55	0	0	0	0	...	0	0	0	0	719	1	0	0	0	0
	Jun	169	6861	0	2132	0	14	0	0	0	0	...	0	0	0	0	197	4	0	0	0	0
	Jul	103	5562	0	1804	1	606	0	0	1	0	...	0	0	0	0	15	0	5	0	0	0
	Ago	101	3482	0	1102	0	630	0	0	0	0	...	0	0	0	0	8	0	0	0	0	0
	Set	251	4775	0	1670	0	0	0	0	2	53	...	0	0	0	0	2	0	50	0	0	0
	Out	81	2597	34	23	0	0	0	0	0	284	...	0	0	0	0	206	0	0	0	0	0
	Nov	1	32	2460	0	0	0	22	0	26	137	...	0	0	0	0	323	3	0	0	0	0
	Dez	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
Atributos	AREAS	6487.85	12.2498	3.09709	33.5636	0.17066	15.3531	1.68336	4.22837	5.01093	0.890325	...	27.7603	25.9865	3.4904	31.7648	32.1171	253.974	78.3785	7.13004	68.5239	22.7455
	BIOME_NAME	N/A	Tropical & Subtropical Moist Broadleaf Forests	Tropical & Subtropical Moist Broadleaf Forests	Tropical & Subtropical Moist Broadleaf Forests	Tropical & Subtropical Moist Broadleaf Forests	Tropical & Subtropical Moist Broadleaf Forests	Tropical & Subtropical Moist Broadleaf Forests	Tropical & Subtropical Moist Broadleaf Forests	Tropical & Subtropical Moist Broadleaf Forests	Tropical & Subtropical Moist Broadleaf Forests	...	Deserts & Xeric Shrublands	Deserts & Xeric Shrublands	Deserts & Xeric Shrublands	Deserts & Xeric Shrublands	Deserts & Xeric Shrublands	Deserts & Xeric Shrublands	Deserts & Xeric Shrublands	Deserts & Xeric Shrublands	Deserts & Xeric Shrublands	Deserts & Xeric Shrublands
	BIOME_NUM	0	1	1	1	1	1	1	1	1	1	...	13	13	13	13	13	13	13	13	13	13
	ECO_NAME	Rock and Ice	Albertine Rift montane forests	Cameroon Highlands forests	Central Congolian lowland forests	Comoros forests	Congolian coastal forests	Cross-Niger transition forests	Cross-Sanaga-Bioko coastal forests	East African montane forests	Eastern Arc forests	...	Red Sea-Arabian Desert shrublands	Registan-North Pakistan sandy desert	Saharan Atlantic coastal desert	South Arabian plains and plateau desert	South Iran Nubo-Sindian desert and semi-desert	South Sahara desert	Taklimakan desert	Tibesti-Jebel Uweinat montane xeric woodlands	West Sahara desert	West Saharan montane xeric woodlands

	Ass.j	Roub.j	RD.j	RO.j	T.j
0	4.5	1.4	7.4	0.6	3.5
1	7.2	1.7	6.8	1.3	3.0
2	2.6	0.8	4.7	1.9	2.6
3	6.1	1.4	4.7	0.9	3.7
4	9.6	2.4	2.9	0.8	2.3
5	2.6	0.8	6.5	2.3	2.0
6	5.9	1.8	4.0	0.8	3.7
7	7.5	1.8	4.0	1.4	3.3
8	5.0	1.9	3.7	1.3	2.8
9	4.8	1.1	5.9	0.6	2.8
10	2.4	0.8	6.1	0.7	1.3
11	2.3	1.2	6.7	1.1	1.6
12	1.0	0.5	4.2	2.8	1.8
13	7.6	1.0	2.7	0.4	2.8
14	3.7	1.2	6.1	2.3	2.3
15	2.7	0.7	8.2	1.8	2.1
16	1.4	1.1	9.1	3.3	2.9
17	1.7	0.9	4.9	1.8	1.8
18	0.9	0.6	6.6	4.1	1.3
19	2.7	1.4	4.8	1.4	2.6
20	4.1	1.8	6.6	1.1	2.9
21	7.5	0.8	2.7	0.3	2.5
22	6.9	1.6	3.3	0.7	3.1
23	5.3	1.7	4.1	1.0	2.5
24	3.7	0.5	5.9	2.2	2.0
25	1.6	1.7	4.9	1.1	1.1
26	1.0	1.0	5.9	3.3	1.4
27	1.7	0.8	2.2	0.5	1.3
28	6.9	1.5	2.2	0.5	2.5
29	4.6	1.3	2.9	0.5	2.2
30	2.1	1.3	1.6	0.6	1.4
31	1.6	0.8	4.6	1.8	1.2
32	1.4	0.6	4.6	1.4	1.4
33	2.7	1.4	7.0	3.0	17.1
34	1.5	0.4	3.1	0.7	1.9
35	2.5	1.0	1.4	0.3	1.9
36	1.6	0.7	3.2	0.7	1.6
37	2.2	0.5	1.4	0.1	1.2
38	7.3	2.4	3.2	0.8	3.0
39	3.5	1.4	3.6	0.9	2.0
40	2.1	0.6	5.5	2.1	1.1
41	4.0	1.0	5.5	2.3	2.0
42	0.8	0.4	4.2	1.5	1.6
43	1.5	1.0	3.2	1.4	1.1
44	1.0	0.5	3.7	1.6	1.0
45	1.2	1.5	3.1	1.5	1.2
46	5.9	1.5	3.1	0.6	3.2
47	11.1	1.8	4.4	1.1	3.9
48	6.1	2.2	2.3	0.5	2.4
49	0.9	0.6	8.3	3.4	2.1

	BIOME_NUM	0.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	14.0
2001	Jan	0.005549	27.050378	294.159587	2.457691	0.708703	0.235960	0.054303	816.822779	136.908757	1317.788732	8.662509	0.000000	87.917982	150.205357	6.904017
	Fev	0.000000	59.446635	130.801451	8.435858	0.742616	0.208993	0.000000	317.193617	20.659474	510.126430	8.036789	0.000000	11.783918	6.511449	62.714495
	Mar	0.000000	44.769512	57.736912	34.308037	4.959537	1.622504	0.003567	145.483777	15.521933	320.159545	3.064967	0.000000	13.559701	9.062745	85.992960
	Abr	0.000000	23.675130	52.096810	137.597482	21.573224	6.919239	2.560974	132.973701	42.203437	289.126569	0.702079	0.000000	20.769079	42.457831	64.666416
	Mai	0.000000	9.948100	58.293280	194.406676	1.519144	2.179819	1.100731	360.944364	11.273052	95.712628	6.766259	0.000000	2.653051	42.684068	25.989465
	Jun	0.012331	46.332785	43.506737	3.371022	7.151395	3.089949	4.647092	726.834858	40.303456	405.066977	49.474295	0.206685	14.910511	37.437618	2.385681
	Jul	0.025586	27.403217	23.248225	0.016606	25.104973	4.184353	32.171323	530.560284	161.060291	787.093604	26.263268	3.521676	20.975495	42.202259	2.349534
	Ago	0.021425	82.596794	94.099520	0.000000	50.715174	17.067755	17.046473	1240.009087	187.251154	1231.583918	54.378242	3.269339	35.227296	119.378443	10.590978
	Set	0.002774	60.699336	81.425340	0.531393	34.585260	10.092336	2.031418	1041.333825	59.118856	602.505641	41.957172	0.028093	30.625437	147.242173	5.385856
	Out	0.001387	33.984645	63.303647	0.249090	27.503074	2.988824	0.386861	815.557296	13.036188	485.267852	7.249867	0.011538	16.470772	85.292188	7.373923
	Nov	0.001695	32.765022	30.459607	0.730665	5.721531	0.242701	0.000396	800.815227	2.544155	123.222787	0.434822	0.000000	5.075402	103.090578	2.927881
	Dez	0.000000	27.441196	17.546983	0.232484	12.718589	0.168543	0.000000	1148.667716	18.591775	493.478286	3.586754	0.000000	4.198134	95.979580	3.036321
2002	Jan	0.000000	38.795751	314.488409	2.457691	10.087252	0.020225	0.000000	772.452931	17.127363	1716.037356	5.985700	0.000000	21.373149	71.646836	30.182481
	Fev	0.003391	42.650640	112.967112	3.819385	3.579504	2.546118	0.001189	357.576610	21.891082	1204.121108	5.519062	0.000000	15.751352	21.735638	72.835567
	Mar	0.000617	43.605021	71.166163	42.528017	34.784582	13.964323	1.120946	153.289352	17.100661	591.558397	2.814679	0.000000	15.317272	14.328885	77.064729
	Abr	0.000000	32.785237	50.562214	98.440487	14.291024	10.946285	3.412783	144.868016	23.242847	426.961466	2.555907	0.000000	18.349764	46.175674	43.448313
	Mai	0.000154	21.687960	48.083014	259.369427	17.277404	18.759924	7.086776	436.149574	22.342505	332.981836	26.929288	0.170064	5.497340	34.714736	44.677301
	Jun	0.005086	18.960196	23.932985	53.238900	17.788168	41.270484	19.215829	479.351371	30.773513	481.091611	22.337141	0.892459	17.296436	15.433147	6.940163
	Jul	0.011868	40.711863	40.098220	13.185180	24.835749	36.196226	57.357341	814.174340	111.809319	883.222909	69.032815	1.341447	33.026539	72.953225	16.193714
	Ago	0.008632	116.581438	106.217332	1.129209	54.015764	34.587205	46.529981	1027.067217	222.770864	974.996044	52.233826	8.248848	23.753002	190.515342	51.545170
	Set	0.002158	134.690165	92.635233	0.033212	46.525244	56.965179	11.333530	846.622593	330.111849	554.654062	54.346426	0.579421	21.309403	127.365858	57.979280
	Out	0.000925	92.179191	123.950790	0.182666	18.431815	3.498946	0.925138	741.187762	74.974558	524.568080	16.578396	0.000000	30.959345	244.736296	73.305474
	Nov	0.000000	42.109743	54.890877	0.780483	12.106087	3.285459	0.116930	684.890193	18.111982	972.666440	6.429007	0.000000	63.445560	226.732565	11.458499
	Dez	0.001233	49.350906	36.506285	0.448363	5.864102	0.294388	0.000000	1334.297312	6.571080	2094.616927	4.397008	0.000000	72.807126	60.272696	15.868394
2003	Jan	0.002929	69.941757	263.764176	1.942904	27.791677	1.289913	0.000000	998.604149	24.766503	735.198363	69.471880	0.000000	37.652683	16.461863	54.364612
	Fev	0.000000	50.174197	229.266318	8.286404	8.082951	1.269688	0.008324	377.099455	21.467196	400.701337	18.175573	0.000000	46.558920	4.743344	66.039990
	Mar	0.000308	141.255666	131.024609	156.827253	45.210960	25.045442	31.788821	174.533097	32.840379	586.880249	4.503062	0.318556	28.200050	8.927324	230.182079
	Abr	0.002620	68.361945	64.064831	141.184382	39.548257	7.528240	12.111613	118.742724	74.783476	536.263449	3.637660	0.024080	9.224968	12.533053	172.347386
	Mai	0.000617	53.719126	100.271534	371.842000	42.043943	91.251255	65.871461	489.978533	116.057365	307.128915	22.721057	1.783413	6.914932	14.200697	126.585685
	Jun	0.001541	29.766503	26.632286	85.238701	27.395108	13.573304	39.553794	516.877145	24.638837	435.361298	22.731662	4.474334	22.478081	7.319955	21.037370
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2015	Jul	0.009865	49.177549	28.506206	3.121932	11.771806	29.641040	51.616652	877.116669	55.152845	834.954653	26.581431	3.372682	29.053033	22.235128	19.699942
	Ago	0.003545	75.837109	81.257207	0.714059	17.249720	81.147683	14.210811	1004.760086	138.258019	645.641193	31.228727	4.995562	25.128096	44.712165	43.845927
	Set	0.010481	117.677322	155.248006	0.016606	43.558242	19.568929	3.762781	932.665415	63.716526	400.275190	23.022251	0.428420	37.601079	25.200723	126.260365
	Out	0.001079	98.924786	122.880852	0.066424	37.222133	3.355123	0.590598	558.773372	42.068260	398.447493	20.385743	0.135951	28.330578	99.578076	85.089292
	Nov	0.001541	68.564705	84.552616	0.464969	13.839779	0.793274	0.000000	727.556957	9.015938	1035.272177	8.745231	0.000000	57.073988	91.097199	14.747847
	Dez	0.000000	63.341340	144.420233	1.345088	1.745458	1.155079	0.000000	806.393262	3.884238	1914.820751	5.898736	0.000000	7.066708	18.853225	32.387428
2016	Jan	0.000000	83.399258	207.277563	3.786173	4.299972	0.444953	0.000000	992.973780	3.777432	1048.236517	5.807529	0.000000	30.476696	9.644612	38.532364
	Fev	0.000000	94.105716	147.737041	17.868078	5.158859	1.487670	0.000000	290.776033	9.225379	373.768844	2.536817	0.000000	12.211927	4.665387	102.403554
	Mar	0.000000	86.784919	87.951962	32.514587	17.540399	8.584441	1.627513	102.840953	29.753014	152.816333	1.628993	0.000502	11.213238	6.606284	151.816070
	Abr	0.000000	93.673856	107.751929	267.655831	24.626737	10.206945	3.311311	130.202221	56.154986	243.812933	1.022363	0.006522	9.100511	16.272997	144.478293
	Mai	0.000000	34.980680	67.895209	157.275616	20.716413	20.085793	15.270716	270.625179	35.902710	334.165578	14.576092	0.365211	5.454843	17.217326	86.065253
	Jun	0.002158	22.538817	27.796990	60.528943	3.630027	13.440717	13.304699	585.744361	13.601092	758.920549	16.287808	2.212335	24.126372	3.559517	25.953319
	Jul	0.006628	50.751236	51.726918	1.295270	8.793731	6.872047	39.127295	784.563820	49.426201	614.863906	18.928558	7.814909	19.582188	9.605633	14.422527
	Ago	0.010173	78.819089	110.866977	3.653324	19.042240	16.083466	5.492955	961.328354	62.429846	467.663244	28.235876	8.049688	32.039992	16.054797	31.989815
	Set	0.000771	83.842144	86.081099	0.415151	24.799760	8.445112	19.959427	778.828782	33.844189	541.642372	18.355865	0.367719	30.786320	9.806956	11.386205
	Out	0.000308	61.889555	118.069188	0.315514	18.074003	3.098938	0.199773	642.144371	7.717577	592.751609	7.661357	0.007023	20.829790	20.329995	16.410594
	Nov	0.000154	36.569680	65.409896	1.378300	7.731368	0.961817	0.177972	744.282711	5.754180	735.075254	3.260107	0.000000	25.817161	46.270509	13.771886
	Dez	0.000000	32.838530	21.404874	3.038902	3.268754	1.341600	0.000000	1010.840306	42.863465	1370.735136	4.015213	0.000000	41.022118	12.427770	22.916997
2017	Jan	0.000462	28.227121	85.338256	21.471584	13.039720	0.961817	0.000000	504.634307	48.590944	988.850558	5.652690	0.000000	27.574732	2.441994	29.061933
	Fev	0.002620	76.634060	324.906548	24.942242	5.839187	3.301189	0.072140	273.661670	18.064419	526.282139	2.203807	0.000000	14.904440	5.218321	74.389874
	Mar	0.000154	94.194538	140.553171	79.526230	45.869832	10.694595	3.043362	111.447128	58.855179	548.839522	2.218654	0.049163	56.041908	7.587582	131.031727
	Abr	0.000308	60.064715	157.430679	259.585305	56.259529	0.991031	9.359982	97.633710	67.168533	379.753842	1.677778	0.003010	46.762301	11.181658	108.620783
	Mai	0.000000	16.789256	54.548497	202.360960	6.187309	2.905676	2.308484	323.876235	23.780216	126.234227	10.959643	0.007023	29.593357	31.725031	39.508325
	Jun	0.026049	16.990791	24.217283	191.616865	12.124773	13.229477	10.505900	572.259980	34.535091	345.463210	20.595730	1.250646	38.171758	36.515390	17.928755
	Jul	0.015876	44.176546	27.524920	0.813695	6.546505	57.333726	25.785733	718.447484	101.440781	719.885479	26.687485	5.416959	48.623079	102.125755	27.543773
	Ago	0.015568	64.544423	54.050211	0.631029	11.328866	88.520863	40.606564	884.540316	113.463145	663.113222	36.071162	0.864867	44.813492	75.250154	8.530617
	Set	0.038688	135.997998	130.547723	0.033212	14.984500	44.758194	5.097770	1082.794121	100.344350	588.764766	25.892078	0.137456	18.040140	134.035231	8.133004
	Out	0.012485	63.255581	88.040614	0.365332	17.111302	3.964124	0.120894	723.049544	23.725978	368.456211	28.800085	0.000000	66.311098	105.064426	7.554657
	Nov	0.000154	34.774244	41.522766	0.298908	8.023431	3.116916	0.002775	641.145570	6.818069	280.262711	8.420705	0.000000	36.271518	130.560101	7.988417
	Dez	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000

BIOME_NUM	0.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	14.0
Jan	0.023428	806.170362	4464.016780	94.720738	98.663361	13.905895	0.156568	13382.845022	337.849452	20120.968649	199.659802	0.000000	446.268071	421.769032	611.059681
Fev	0.017571	1351.098356	3039.033593	232.351432	113.069964	33.090546	0.274291	5941.738253	172.039626	12349.343812	119.518867	0.000000	347.631642	175.038796	1724.630552
Mar	0.024970	2169.837995	1918.588391	1156.991232	463.513904	237.714845	141.921417	2670.725290	536.600621	8232.005701	65.673017	0.432935	347.604322	169.724032	2538.617713
Abr	0.006320	989.661960	1450.297921	3062.116878	823.148837	317.206315	169.486078	2242.725916	1250.841515	6832.633484	41.762032	0.256350	472.962500	304.549687	1943.209546
Mai	0.006628	424.426734	1045.702389	4307.335894	465.541044	297.489068	191.173300	5807.769091	699.840424	4055.726718	267.962964	4.030362	223.384467	351.173013	1329.655744
Jun	0.082770	439.594524	505.398977	1337.399034	590.088674	223.062867	320.103550	10116.740886	830.667074	9863.590182	443.117877	39.181902	363.662262	487.625324	422.663167
Jul	0.200220	799.173001	662.309972	39.788024	230.270337	315.574821	690.642954	13816.263759	1425.689808	12668.120747	533.465470	83.145156	478.414307	645.083528	360.382432
Ago	0.214555	1590.655214	1661.586223	15.692689	498.371156	564.002239	350.269219	17947.039070	1871.647079	13289.007531	800.622421	82.119756	638.653731	1033.269412	472.907057
Set	0.117142	2064.521677	2393.454394	5.330532	517.016136	275.463911	94.390194	17672.101896	1462.682274	9077.699260	622.992203	9.766379	454.360788	1496.675312	535.079352
Out	0.038379	1258.992673	1526.483621	3.437446	420.348771	72.295821	15.445517	12094.693021	847.861190	6862.141799	296.449128	0.315546	439.820611	1986.376150	521.560493
Nov	0.008632	755.936134	942.285238	12.620575	162.728312	28.414049	0.742013	13192.053837	134.414667	8729.243551	69.189775	0.000000	369.660464	1473.536842	183.589004
Dez	0.004932	748.146110	881.479132	24.410849	74.074689	11.020444	0.020215	17611.936057	134.546506	18756.852998	83.021367	0.000000	344.250064	448.362941	295.643722

	2001										...	2017
	Jan	Fev	Mar	Abr	Mai	Jun	Jul	Ago	Set	Out	...	Mar	Abr	Mai	Jun	Jul	Ago	Set	Out	Nov	Dez
Eco_Id
0	36	0	0	0	0	80	166	139	18	9	...	1	2	0	169	103	101	251	81	1	0
1	1656	965	47	519	3407	28944	4019	5190	4374	707	...	2	293	4934	6861	5562	3482	4775	2597	32	0
2	1332	1335	1294	363	0	0	0	0	0	195	...	842	76	0	0	0	0	0	34	2460	0
3	0	0	24	114	36	34	343	616	181	0	...	621	383	886	2132	1804	1102	1670	23	0	0
4	0	0	0	0	0	0	8	0	5	0	...	0	0	0	0	1	0	0	0	0	0
5	0	0	0	0	0	4	0	6	0	0	...	69	23	55	14	606	630	0	0	0	0
6	378	91	0	0	0	0	0	0	0	0	...	19	0	0	0	0	0	0	0	22	0
7	1678	185	30	0	0	0	0	0	0	0	...	138	0	0	0	0	0	0	0	0	0
8	457	299	232	0	7	0	0	4	1	81	...	45	5	0	0	1	0	2	0	26	0
9	0	0	0	0	0	0	6	42	103	674	...	0	0	0	0	0	0	53	284	137	0
10	0	0	0	1	6	0	145	165	72	0	...	131	86	4	8	302	49	117	11	0	0
11	5962	22554	806	0	0	0	0	0	0	87	...	462	13	0	0	0	0	0	70	510	0
12	11	137	328	18	0	0	0	0	0	0	...	65	0	0	0	3	0	0	0	0	0
13	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
14	1442	1692	497	0	0	0	0	0	0	0	...	126	36	5	0	0	0	0	200	1728	0
15	0	0	0	0	0	0	0	0	0	0	...	0	0	0	504	0	0	0	0	4	0
16	0	0	0	0	12	582	128	384	23	0	...	2	1	43	41	333	126	72	0	2	0
17	0	0	0	0	0	0	0	53	1125	1289	...	0	0	0	0	0	15	432	1265	3564	0
18	0	0	3	694	5225	17107	11941	25878	29019	20591	...	0	137	2869	7230	15259	11598	20766	13980	4770	0
19	8	3	7	276	1613	1765	1338	1486	1311	97	...	134	961	116	1704	1525	2609	895	448	269	0
20	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
21	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
22	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	9	0
23	174	830	0	0	0	0	0	0	0	3	...	34	7	0	0	0	0	0	0	358	0
24	8155	9762	481	30	12	0	0	0	37	120	...	6263	82	23	21	32	7	242	392	6806	0
25	0	1572	449	25	137	5473	8527	3704	2488	1036	...	4	0	42	61	1007	3969	4300	1919	35	0
26	303	1196	701	48	1	13	471	3563	952	0	...	2137	965	118	70	3679	3448	1244	10	193	0
27	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
28	12	0	0	3	726	14960	6347	21252	18441	8886	...	0	0	50	625	5599	12056	20484	42922	6511	0
29	177	104	243	157	0	38	538	1259	106	0	...	2069	583	332	55	1553	161	125	20	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
817	0	3	945	1354	941	6647	5439	2406	1678	600	...	669	521	0	1079	5259	3139	3207	1726	188	0
818	0	270	2784	646	640	172	88	245	414	256	...	3509	1330	0	0	0	47	131	1375	263	0
819	0	519	255	16	2	94	183	50	7	41	...	116	38	39	3	57	13	20	88	8	0
820	1	24	0	0	0	81	7	2	2	0	...	1	0	54	103	133	2	0	6	1	0
821	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
822	0	0	0	0	0	0	0	0	0	0	...	0	0	335	141	0	0	0	0	0	0
823	3	4	14	175	305	0	0	0	0	0	...	30	35	5	0	0	0	0	61	0	0
824	0	0	0	0	0	0	0	0	0	0	...	4	248	0	0	0	0	0	0	0	0
825	0	0	0	0	0	0	0	0	0	0	...	0	0	21	3	0	0	0	0	0	0
826	0	0	0	1	125	0	62	16	247	249	...	0	0	0	22	1	0	0	0	0	0
827	0	0	0	61	18	0	0	145	162	219	...	3	20	20	510	1407	659	204	5	34	0
828	0	0	127	183	139	4836	33039	35184	2755	28	...	6	215	124	57569	149284	73794	37386	1132	1	0
829	0	2	1	0	0	8	18	54	0	0	...	4	0	26	8	195	7	7	14	2	0
830	106	25	2	120	93	80	63	0	10	44	...	7	137	591	380	193	82	115	29	14	0
831	0	0	0	0	97	579	466	368	108	64	...	0	124	473	105	42	76	39	18	0	0
832	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
833	0	0	0	0	0	0	0	0	0	1	...	0	0	5	0	65	0	0	0	12	0
834	0	0	0	0	0	0	0	0	0	0	...	2	0	0	0	0	0	0	0	0	0
835	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
836	0	0	0	0	0	1	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
837	0	0	0	0	4	2	0	7	6	0	...	0	0	0	0	0	0	0	0	0	0
838	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
839	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
840	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
841	107	13	26	41	84	187	23	2	0	55	...	57	221	719	197	15	8	2	206	323	0
842	0	0	0	0	0	0	0	0	0	3	...	0	1	1	4	0	0	0	0	3	0
843	0	0	0	0	0	0	0	11	0	0	...	0	18	0	0	5	0	50	0	0	0
844	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
845	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
846	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0

OLD SHAPE¶

Índice

clicar aqui! para ir para as primeiras figuras

outras figuras:

Não dividido por área¶

Total por Bioma¶

RESULTADO¶

Total por Ecoregião¶

Dividido por área¶

Total por ecoregião¶

Total por Bioma¶

RESULTADO¶

SOMAS MENSAIS¶

Por ecoregião¶

RESULTADO¶

Por Bioma¶

RESULTADO¶

SOMAS ANUAIS¶

Por Ecoregião¶

Por bioma¶

Gráficos totais por ano e por meses¶

sem áreas¶

TODOS OS PIXEIS¶

biomes again¶

todos os gráficos com a mesma escala?¶

Comparar fórmulas de logaritmos¶

escala horizontal, ano sim, ano não¶

Alternativa¶

Test seaborne style¶

PLOTS

Variação inter-anual. Totais.¶

Variação intra-anual. Totais¶

Variação intermensal, totais¶

Variação inter_anual em cada bioma¶

Variação intra_anual em cada bioma

De novo¶

Variação inter-anual por mês em cada bioma

Lost Pixels¶

Mapas¶

Total por Ecoregiões¶

Total por biomas¶

Variação interanual por ecorregiões¶

Variação inter-anual por biomas¶